{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 聚类效果可视化评估T-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "方法一："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-31.641825   -8.913283 ]\n",
      " [-10.724953   -4.4100676]\n",
      " [-42.90533    -6.0672812]\n",
      " ...\n",
      " [ 28.71072    -8.941949 ]\n",
      " [-31.823803    6.9806647]\n",
      " [-12.525891   -5.416681 ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.manifold import TSNE\n",
    "datas = pd.read_csv('C:/Users/yc/Desktop/diabetes2.csv')\n",
    "X = datas.values\n",
    "tsne = TSNE(n_components=2)\n",
    "tsne.fit_transform(X)\n",
    "print(tsne.embedding_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Org data dimension is 2 ,Embedded data dimension is 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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HP1yJM59+AHtuEaZ960VkDBup9zIFMTEMau54AjV3PKH3UohLZOx7EaG8SvjGLYfzwO/0Xg5hIEwsy7KJbtDR0YtwOOFNCMKQ1LZuwZ+O/xZ3VTyISW12NLz7Eq74/q/QsncdcsdMQM4f/gZ252dxSkAEAfTXP7O2P4W277lQ8MYUMH3tAABT0A2MzwXurDSmkTuhCIYxobBQehc8nUSJQUlk5vKGEUsxe9jVOPnpb8DYnPjyNz+CPW84Sr2ZYA/UwfKHtxC8ZwXCNy2lph0iCr/+2XXrx0A4CFvLVmRv+D46F3yEcHulYYzcCf2gmigxKInMXK4/twaP7noQR1r3wt/TgYl3PYNgXw9O7v4QpkmTwXCVgAgCEKx/hrNGIpwzFsjqNwPIGfF/kZ31LPb9oRarHl2Dva/tA0sZuyEJBVFiUBKZuYz8b2LxzMtjI9Wz4fZ2wWS399/4khIQQQCx9U8+wTHz4XnyebhczyDgvohxs9bjmqcW4syec2g52KrDagm9oSBKDAm4YyOuk4eQYc8F6/f3/5KUgAgOjsNvw35iDXLX/jMAIGf17dHfcQ0MGOfVGDsnCFumFSbGBKvDqteSCR2hmigxJBg+/VpcPP4F9rz0ELJHVaFsxnKwf/xTjBKQz+fD7t3b0d19EeXlVZg4careyybSAL9rm7lU/7Se34mcjQ+jZ/GvorcVMjBY/5NNyB2dg4KKfB1fBaEXdBIlhgSRsZHFL27BzMd+A8fX7oBp4qQYJaCGhnoAwNy516ChoR4dHW06r5rQmrYDtWj9fANmP/FHFM+4Ht6Os9H6ZzhjOACAzSyO3p5vYOB234mFT8xHyB/G0bXH9HoZhI7QSZQYmnCV2pj+vWRPTzfy8wtRUDAMmZnZ6OxsR2FhkT7rI9LCxWOfx3Rtj1pwW/R3gdGL0fY9F+8elw0MmrY2o/XgYUy5YxJMDBDyBdO4csIo0EmUGJKE138cHXGJmF07nRlwubrh9/vh9fYhGKSL4mAn2OeK6dpuWve65PuWTi+Br9ePjc9ugS3DhvE3j9dwpYRRoZMoMeRwfrkSWc1P4dyk+2JGXCr/6TbU1m7C+vWrYDYzsFqpUWSwEyP2Xz0brlOHJd/XkevAoh/P13B1xECATqLEkII7RM8fcQkE/KiunoQFC64Dy4JSuUMAftd2ZklZ9HdswI/A44/BP382gs+T+D8hDAVRYujAG6Lnj7hYLFY0NjagtnYzKiurkZ9fqONiiXSQSOxfKOVPEHwonUsMGfgi4uzh+pgRl5ycXNx4Y/yAPTF4SST2zx46FKNqZV5Vi6w9l88d/ppC+BaXpXG1hBGhIEoMGRyH3wbT1w77iTUAgMLF59BxacSFnTETj79Th2mj83D3nDE6r5QwBB53TMo/MNyCngemgXH5kfHhUfinl+i7PsIQUBAlhgxdvCH63u9uhu2JYnT0+vDKR4dxtLUX00bn6WKZVlCwAmZzCwCQM4hRcGaA7enp/2+fH6acbMBugW3/KfgnF4HNd+i7PsIQUE2UGDKIDdEfu+BGeVEGJpRmw3PhVOzwfetJzZtLTCY3GKYVHR1vob39I3g839DkeQh5mGomxKX8TZ4ALE1dCEwZrvfy0oLzy5UoWpmj9zIMDQVRYtDD77LkD9HPKS/AfXPLYDaZ4O1siQ7fdzXuR9aRU5o3l5jNJ2AyscjNfQo5Oc/BZPKo/hyEfJgly+JUrSwnuxEqygCbOfgt0Ph2cIQwFESJQY+cLstw0BczfN+97r00WKbZ4fHcCpfrGZhMvcjIeFOD5yDkYrLZYH3xZdi274blp/1ZCKbVjVDJEDArELCDI4ShIEqoQtAbxI6VuwzprcjvskwUCE0Wa8zwfbirU3PLNK4zSCAwAxbLKdWfg1AHxu0Hm23Xexmak8gOjoiFgiihCqd2nIanzWNMb0Vel2WiQOjIK44Zvmdy8zW3THM4PkBBwT1gmLOwWusQDFao/hyEOvQtG4/A5MEvwpHIDo6IhbpzCVUoXzwOo2aPRDgYNp63Ir/LUiQQvnDrZLDhMI76b45apuVc/1Wwf3ojprlEbbzepbDZ9iI//2EEAtPg8dyl+nMQhBz4nexcOzgiFgqihGrYMm1Y9egaw3krmmomILx1S0wgFBsp4Q/fs34/gnv2xTSXqM9lZxCCMALhrP6xrnD3CQCxdnBELCaWZRMWrzo6ehE2UH2LMC4sy6L7tAv7/vAFRswoxYRbqvVeEoBLgfDfngS78zMwNy2B9ZknUVh4Czo73wTL5oJlbQD06bak+VDjose8MKE/DGNCYaH0sg2dRAlV6PdWvGBIb8VIl+Xlf6jDw79/BDPK3sdDi07B5Xr6UiBN87o486GXgzlhBLhm3S1718HbcZaCKCEINRYRqjBQvBU7en145u8+HDwzBj7fAt1GSpxfrsSwX5XSfKhB4Zp1dzXuR9bIKr2XZGiM3J2vNRRECVWIeCt+9dfLMf9Hc2HPMuap6tgFN8YNK8KE0kywbIEuIyXcIXaaDzUmSsy69aSgYAWKihajqGhxWj9P3O78vBOvYfiruWl7br2hdC6hmKA3iD2v7UPbkXaMmF6CK++fARNj0ntZgswpL8A1E7fiyXf8MJkmwWqtQyAwKX0L4AyxOw/8Dm73QwCcl4J5A3w+H3bv3o7u7osoL6/CxIlT07e2NMEG/GCeWQT/dhcyvuaA9aePGa4WrMSsWy/0LA9EuvOZjqOYWfJO2p7XCNBJlFCMoWdEBfB6l4JlM+BwrEU4nJXWkRL+EDt/PrShoR4AMHfuNWhoqEdHR1va1pYu2PUfIrC/B9Y//Abud4Hebcabi01k1m1U9JaPtDkZWP52D04Gr0/r8+oNBVFCMeWLx2HBE/Ngy7RGZ0T1SitJw45gcBz6+r4Jl+sFsGz6ZNz4Q+z481Hk5z8cDeY9Pd3Izy9EQcEwZGZmo7OzPW1rSxuHd8I23Yr8Wb+FZbwNbP1+vVcURyKzbi7G+pzrKx/p3Psi7JVTcbrvqrQ+r95QOpdQBe6MaGGljbpOReAPsV9csAHec7nYubMW3d1rYbc7wLKA3++H19uHYNA4Xc5qwbr9CFvG9F/sHXfD4t+BML6r97JiSGTWHb2Nwbqrg8EqBIOjwS0PpIumrc2YfPB1ZFhcmJ/zDwD9Kkeu5YM/tUsnUUIVWJbFwifmI+QP49yebYbvOn3h1sm6mG8L2bGdPt2MYDCI665bimAwgM7OdqxfvwpmMwOr1UDKTyrBOkoR8IxGKFSJsNcBc47xPh9SEEqf+nxebN26AatW/Q27d38Klg2nbT1c+UizeT+amsxpW0fp9BJscf8n/lb/AvYHfgAA6L7mv/H4O3V4c+fg1oKmIEoopmlrM3b9eg+sTgtMDBDoM1PXaRK4dmy5uXmwWCxwOp0wmRiMHTsOCxZcB5YFCgsHn06rbUofmIZa4Ph2hBvbYZo4Qe8lpUh8+pS7IWpvb0Vra0vaVuP1LkUwWIX8/Ifh8TA4eHBO2tbhyHVg9pNfw3Wv3I/xX58HAHh2UyeOtqpv2GA0KJ1LKKZ0eglO7TyDjc9uQWFFAUpnzoXbvQh6pJWMSDLlm4yMLHg8bqxa9S6ysrJw/vw5nDrVjPHja5CfX6jTqlPDsbkZ1qMd0Z/9NYXwLS6LuU3o+idhrt2BwP3fgv0rYxCY8q/AABwrFEqfcjdEDGOGxZLOS2y/fOTxD1fidO17CNmagZoZaV9HYPRifLSkGeUtLniDobQ9r15QECUUE5kRjf7s+AAZGX9GV9fL6R8hMRhSlG+am4/DarVi7txF2Lt3B8aNq0BV1USdVqwM74Ix8M4bBcblR8aHR+GfXhIvbYi7gRc2wgogDOgeQNmAH8GnLslCLlkGy9PPSrqf0OecuyHKzc1FYeEwjVcfS+TzNvUHr2Lv33+Hzav/gqxxU9O+jjnlBZhTXoCn3tfCf9dYUDqXUB1uWindIyRGQ4ryDcOYwTBmmM0WMAwDv9+nw0pVwsoAdgts+1vgn1wEFISizTft7R/B4/lG9KZ6q9z4Ql68XP9zfHfX/fjFlafQ+/tXk5q2cxH6nEc2RIsX34hwOIxjx45o/CpiiXzeDvzuSZi7zuLqm+/QZR1DCQqihAb0p5U6OtamfYTEaEhRvqmoqIbNZsPmzf8Aw5hRWVmjw0rVw+QJwNLUhcCU4QlnF/WeL97e+gku9LXg3/dVwZ+TgU2OI0lN22OJ/5zrvSGKfN7yr3sACHjRsvWvcetwfrkSRStz0rquwQylcwlCQ6Qo39hsNsyff60Oq9MGy8luhIoywGbaEGm+8XqXIDv7JWRkvAm3+1EA+nvQzh2+CFcUzkLOR7+EPWRGiA0lNW1PRkVFNTo62rB58z+Qm5uf9g1R5PM2Ye4SdB76FGeP7IVjxIzoOriSk4Q6UBAlCA3JGzcV53euuax8U1qu95I0h2l1I1TSn31INruopwet0+KE0+LE2ko3TuT7cNfwRYBvjahpuxRJRr03RJHPm7/jNHLDbmROmYPqRTf0/5InOZkOXrhVfRN7o0HpXILQkETKNz6fD7W1m7B69buor6/Tb5Eqw7j9YLPtAGJnFyPShly488VH1x5L+1prW7fg/bLz+ObHvRh5zhc1bRdCjiSjXvVeoc9bKMzi8XfqcOzvP42RnCTUgU6iBKEhiZRvuBflbds2ori4dFDMhfYtu2yD5/Uuhc22F/n5DyMQmBbTZKa3B+2p3ma82fh73FCyBDPD++B55G44liwDM3OW4O2FJBmzsnIET6fceu/m//gEI2eNROnUkoTrcX65Elnbn4rOD6cC//PW0evDKx8dxtHWXkyy/x32ti7YT6wBMHQUhbSGgigxZJEy06jlc84F0Fqcj4yCYbCxQex543nkeloFZ0kHLv3NN3xCYRa/PX0RM8670HppvjjdHrRbWtYjxIawvvUfWL8cmHP/3Xi46vuit3c6M+BydcdIMopthOTWe7WqVR674EZ5UQa8wRD+WvQn3Da9OCo52bP4V6o/31DExLJswjxDR0cvwkPIYJUYQgTCQDgcnWl03zYBwVw7fvzeAUwbnaeNLCDnOe0f1GNHpQfFBSx2HW9GeVklstqOIK98CgqqZ6v/3Aaho9eHVzY14ovTXbhj5ihd5BdToafHhdraTQiHQzAxDI548jEmw4sJY0oxefJ0bNiwBmVl5Rg//rIC06pH1yBnRDYWPjkfjFmkehYOIu+96xEsvhLOA79TdBIV46n3D2LiiBzcPWcMrKe3IG/VVzV5nsEAw5hQWCh9ooBqosSQIjIb+J0d9+KFw8+hy9QTnWlst5rw3KXUl5rEjBRw5ij7agpwztuB3SfOwMSyuLB7jegs6WAicjqaUJot+Huj1ooDAT+qqydhypUL4PYFcaADYBl73Ok0gtR6L98eT2u4kpOEciiIEpqh9zC9EJHZwJ9d8SJ8YR+2N62LzjQmu7inglCaLjJH2V2RgerqSRjtPQOWZVF9zT+JzpIOJuaUF+C+uWUwm4SN243qqWqxWNHY2IAv9mxDKHMEhg8bBn/WSHR1dcYZBvD1pBPVe/n2eDmrb0/L60kVY9m/6Q/VRAnNEGuu8Pm8l6y/LqKkZCRmzboaJlN69nOR2cACeyEcZgdGXsiIzjSqLlUmMlIQmaM0ZTvReGgv+hyjkNN9EqMn3g/fceFZ0qGEUAOPERqucnJyceONl0+Lde8fhCkcRHX1JAwbVoTt27dE18nXk05U7+Xb4xm5VinX/i2ZbvRggIIooRlizRVcp4utW9ejtbUFJSUj0rKmyGzgx2c/QqOrAVO8X4/ONKoNN03HDaKROcrIRbn18404/D+vo/f8N4bMLGkihBp4jAprMqOxsQGHDx+IMQzg60knIpzVH1jC3Sf6HzOzWPV1qjWvyVWgCoeL4XI9LRpIpehGDwYoiBKqEfQGsee1fWg70o4R00tw5f0zBIfp9XW66J8NfLf5z7ir4kFk77MjeGmmUW0ch98G09ceN1LAuP0IDsuL3m749Gtx8fgX2PPSQ8geVRUzSzoU4GcmamomY/v2zZp4qqoxRsKFtWbgxgXq1DIHRq1SXIGKD1c32p43HKMW3JbmtaYHqokOMSKD10JGuYl+JwUhLVSh5gqu04Xdbk+rw0R0NnDEUswedjW6bh6DwGRtUoVdt36MzhU70XPNywAQTdP1LRsf85yR2b7FL27BzMd+A3vOwLI/S5WIMXq8B+c5VFdPUt1TNZ2Sd8c/XIlPnrweO55fAU/72bQ8ZzoIBqvgdj+EUKjykgKV+LVCim70YICC6BCio9cn2n2a6HdSKV88DguemAdbphUmxoT2Ix2CzRV6Ol1EZwPPrcGjux7EG43x8meRi7tSwlkjEc4Zi3DGcADapOkGA/zMhM3mQGNjA2prN6OyslodT1VOfVotxD4nkTTmgvn/C2OCE2H6wyfIfnUf7FuaVXtuvRBToBLaNPB1o93nm/RcumZQOncIwR28lvM7OXDTtxU3lGPPb/fFNVfo6XRxb+UjuLfykbQ9H5A4TccXfAjl2GF2XX4/0iEAoTd8D84xY8Zi7NhxKT+ekIiGOesvgvVpLYikMXd/+X9hzx+O4q8+iPDHZ+H+4CWEf7pLlmep0RBSoBKrfQ4V3Wg6iQ4hEo0W8H+XamqXm749uf0UFv14Pr766+WY/6O5sGf1NyAMNusvJXgXjEHPA9Pg/voEsFYGfTeWx/zsn55YKm4wIDUzIXV+lP+e+qeXpHWMJJrGvPtZBH29OPneb+DxHkL46CFY/vCWLM9StVGeZo63fxPzzE2kGz2YoCBKxNHnD+K5jw6j8VwX5v7mf8E/fzaCzz+X9H5SZ+MiThdf+codWLToBjgcTrVfgiQM4avIM7FmizJif853qPZURq3TSc1MSJ4f5b+n+Q7R+rQWxKQxy69E74VmBHvPwDRpMpjxVTI9S9WDe2IsnnE9vB3qfAbEap9DpdZPQXSQoGZA6HQHUF6Uga93HkRh8xHJu+fS6SXw9fqx8dktsGXY4mbjDBG0LmEkX0WuibXQz2qg5ALKBvwIPP6Y5M2UXKRmJoTmR8Xgv4fprE/njZuKntMN6D3fiJ5jB5CVNxpswAuT/VIXuELP0lQROzGmAldIpeO4G1mlg7/2KQYF0UGA2gFhZL4T980tw6iWE+gcWyW4exY61URm4/jpWy3WqAgNmkyUEGtiHf+zGii5gIbXfwz2QJ1mqUipmQk586Ni76HSMRIpKWVuGpP1+lA++5uAMwOs33/pQfyinqVaoma3LLcTv6slH90nj1yufZaUqbfoAQAF0YFOCgEhUfcp93d2Xx9C1vjds+xTjcGCVrq1SvnwZdO4JtYA4n5WAyUXUPbQId1TkQBQWVkjKLEnJEOnxXsISEspc9OY82b9BLbhxTDVTAB7uB7hYw0JPUu1RM1uWW4nftg0EQU11w762qcY1J07wBFTxVEDv80BJnBp187ZPcsdotZyjakgJoKQDoRk05zukzHiC3wxBjXgX0BlSQt63LqnIoHLAvBciT0xGTqx91Cp5KRcScKItypTtQzh2q0I3n8XmASepVqidrfs5U78XEx98Gkw5p+qtNKBBZ1EBzhadh2eLxmH/FPH43bPck81RhPYTmeTCR+ubFpOznMwmTxx4gv8n9WAW6eTnXLTORUZcd55+sgP8duz/40N29dE50eF3k9A/D2MF3ZokbWWVCUJTTYbrC++DNv23bD8VP26shTU7pblC6nEOCTVPYsu/0V1Fm5wKIgOQLgNOloGhDt+8giyr5gWt3uWmxbSM2gJoa8IQr9smsv1DEym3rS5YCi5gOqdiow678x4Cc6cTLCTTKiunnTpt/LeT6WSk2IpZSGM1g2tZresUCc+3yFpw7m1Kq7euFA6d4DBb9BRS7xaSFM0snvmIzctlA6B7VTQQ6s0GKxCMDgagPOSbFpDWp43cgGtueMJ2fdlluibiuQ774TYy4IgQu8n3znEXjAiarR+65SCGGEHuZKTQillIeSKr/t8PuzevR3d3RdRXl6FiROnylpXuhFyqalwlIv+nQYzioKokOC4iRH2CCRUQMRaC1AWEOR2zqYqmD4QBLZ9IS9ePfoKjnbXY0xmGb5T8xjybPmqPd6/XlmFwuy/o6vrZVitdQgEJiV/EBVJ5fUJbaaUWFzJvS/feWdF2X14/J06TBudh4ev2YeMjD9H38/Te7JjgteFM814/dNuHG3txbTReVFhh7lzF2Hv3h04duwIqqomSl57xFM04tqSl5WNwOOPgd35WYwSkdy+AW7D0rZtG1FcXGoI+zcxuC41/XXmrdE6c8eIDjS6GnBn+f06rzI9KErnCgmOE9qhSVepxM5Z7qxg6IWfDdoharVTUvzH++B4EMFgFfLzH0Y4nAWP5y6VVp7aelJ5fUpmTlO9b8R552uj7sFrmz1RjWevd2nM+9lyMD9mlOe8dUSM0bpSycmIfd3y5behunqS6PiP3L6BRDOwPp8XW7duwKpVf8Pu3Z+CZcOy1iyGVAWoZHDrzOcunMG2hk24s+IBjM4cq8o6jY6iIMoXHI/4RRLaoEWDjtTAnGxWUKkDjFGYO3wRHp/8EwxzFKmSkuI/nj9sipNNSydqvD4lM6ep3JfrvJMVmIyxwyzRoMiXoQt4vDHBq+DIBzFylmpLToqN/8jtG0jUsKS0GUoMyQpQSYjUmdtCF+AJ9eKKopmYPexq+ELp08TWE8WNRbZMG9b/ZFOMXyShDVo06EgNzIlmBdVwgEkXYuo7kWYtp8WJAnthNHU4b/giRc+n9uMpRcp6kp18lMycpnJfrvPOG62PI1y0XlD/GUgevFSXnBQZ/5HbDZ2oYUkr/105ClCJiBgIfL55J3yMH+u6xR2SBiOK/xqRNud9f/gCR9cew4RbqtVYFyGAFg06Xbd+DISDsJ7fiZyND8cE5oKCFTCb+3e9bf5iBO2j+3/BmxXkO8CEwmy0kUMNSzE14Z6og/esQPimpbBW5MbUhLmm3WqkpNR+PK3Xwz35bN26Hq2tLSgpGRH9vZKZ01TuK+S889RBYcGHtDuHODPA9vT0/zdn/Edu30CihiW+yw2/GSrV3hQ54zqJnuNynXkJ9u7dgR8Pf0ZWnXmgk/JJNLJzlyI4TqhLYPRi7Bj1girt82LjHtwh9vb2jxC0ThKdFeQ6wETE6416Ko07UR/6MqYmzDftlpuScmxuRvar+6L/C244GPN4Lr9L11k6Ka8v2clHycyponlVCaTbOURs/EfuOEmkYUnIQzWZy02qvSlyxnUSPYee1oZGIKWTKLebMyI4zvWLJLRFbvu8FPids9wh9nC4GBcnTEdw67aks4IR8XqlvqSawUu/ZfauQWjEZTUlbupw/bk1mFM0Hw9XfV/yw3sXjIF33igwLj8yPjyKNcP3IdR7+fHKssrRF+zDz654Eb868hI2nFuL28vSJ4co9vq4Sj7DhhUnPPmk2p2t9L5CcLMlbvcD8HjuFhzleeFWbWZb1Rr/iTQsCT7HpSBVsLMdXztRDDT0AZv2Rb1myxePw6jZIxEOhmX1pkgd1wGQ8DkqKqrR0dGGzZv/gdzc/CFnbWhiWZZNdIOOjl6Ew5ybhIPIe+96BIuvhPPA7ww/sjAYaXj/FXQc3gmwYdjzhmPyfT+DLStP1eewWBpgt6+D17sE2dkvwd9bje7Hmvtb+W9aIqi68tT7BzFxRA7unjMm5r+NRPDn/xvshVZYX3oFgTu/geHzdsFs7t85m4Ju+MbepIoEoGPDCYSzbfDPGRXz733BPvSFPCiwF+L/HPh3lGWV45vj7lH8fEo5fvwITp5swpw5C7Bp01rYbHZcffVC7N27A6NHlxkyPWcyuVFYeAs6O9/kSP6pJ9qvN9wNQn39Ndj96UTk5eTjquoZyPvHSbhvmxBjlbfq0TXIGZGNhU/OB2NOnmR0ubqxc2ct/H4fxo+v4QhYiCP1OQby+CPDmFBYKL3hT3Y6V80xi8HS0Zlu1HBjSPbeB4NVcLsfQihUiUBgBqyZZ3WXLVMDfvrt4rRfxzRrXZz7StTiae9r+8CGE+4x43B+uRLDVo4VtTLjN/bMLZirqc2YVLgpXJPJNCDSc2KSf4MBfjmlqOgpLPva7Zh37fXIqu+K85rlS/BJIScnF1+xTMaK02WYudmL7Ff3wb6lWfT2Ys8h1Ig2lMYfZQdRsW5OuQFxIHV0Gg2lbgxS3nuH4wMUFNwDhjkLq7UOwWBF0sdN5A5jFJgly2CaOCmafsO8pTE14ZNf+lL+8kfKHCFcndDKLNLYc2fFAxi5vV5TmzGpcJtXMjIy4XQ648ZAtJpXTB19JBTTgdgGQchrVkiCTyreBWPQ88A0uL8+AayVgX96ieDtEj2H0AjOUBp/lB1EhcYsUgmIkY7Oy/NehFSUNmdIee/5Q+zpFgXQCjEh8EhNOOUvP0e0gjVNELXhimvsOXzAEDZj3OYVACguLo0bA1E6r6h2EOZnSywW42W0uDrXXCPr5FkO4Q2CkE9q6fSSaG+KLcMmrzfFygB2C2z7W+JOt1wSPUcki8EwDAKBAPbs+RT19XVDZvxRdhANZIzAD9d1Y/3J/p/ZzOKUAiK3o5OQh9IORGnvvV2RKMBAOJVy4V7sUvnyc8scLIaBzbYL3o7b2PPorgfxZlmTIWzGpHRYKp1XVBqE+YLuqWRL0glfTlNOilNsgyDkkxqR4Pvqr5dj/o/mwp4lry4sdLrlE3mO+//yNv7p/zyNUeNuigb2SBbj448/BMMwUfGG9vYLslPMAxFZQZR74jyVc3W0qYgCYnpR042BiL/YpVJf4pY5bOxTcDZ/V/B291Y+gtfm/SX6vwcby3W1GYsgRcmHm/K12+2yxduVBGEhuUBDZ0sE5DSlZDkiZbEDLa8JbhAYt190g5YqQqdbIfh1Wo/nGwAuZzEKCoaBYRh0dLTBZnJg/+r9hhl/FBNZUQNZQZRSsMZELQ1MeemmQQLvYpdqfSmZmhT3pMtFb5uxCFKUfJLNKybC+eVKjHrv6pSDsLBcoLJsiZaINWAmynJwDykHTy0S3CBo4TUrdLoVQqxOG8liOJ0ZCIfD6OvzIGQKIBAMpJZi1oBksqVKkBVE+SfOgoIVKCpajKKixYOqqD/QUEsDM5WOOr64QKLuPiPCv9ilWl9K5FHKPenyNyqmm5bGNDql22aMC3+3zg/8qQ7VR17/kYwFKQdhNTrS04lYA2aiLAf3kBIM20Q3CGIbslSRfroVrtNGshjnz59BKBTCqVNNMFvMGH9NRcopZrVJJFuqlJRl/2yWvujR/vKM1mVvRKEhaCG0GoIeSghpYEqxUeK/96kMbfPFBcS6+4yK4/DbYPraYT+xBgAwvPZuLPpx6nOicXZvPPs67kZl8398gpGzRqJUwLNVD7i7dXz/NmQWxG6iUhqq57x+prkl5dEZJVKDeiAkp9m0tRmtBy9gyh2TBLMcc8oLMKe8AE+9L36Bl2tbKIW+ZdI2imJeuJEsRmdnOy5e7JQk3pB2RDSO1SDlIDos+3SMoo3L9XT0oszNnV8OsIRWyNHATIYt04ZVj66R3lRjZQAwsO0/lbC7z6gk0g5WA+5J13ngdymry2gBG/Aj+NSTUS9MWG39u/WKcuQu7oLHPAuZ4dro7SMXSzlwX/+Ugytw2vZgSso2adfEVYiQznXpdG+ckbW8BxX3E04HDscHMd6tfC9cvtcqV7pQd0Q0jtUg5SAaDNvg8dwaVbTJyHgTbvejAOIl41yupymQakhlZQ1qazdJ0sBMRiqGApHuPvedxs0qiBlBayHqz4V/0s1ZfTtcy9+Rt1HRCL4Yv+nKWTBlZyNj34sI+rPhDVQiE7XJHygB3NdvYj1Y2vUqXF+Rf9JXWy4wXXAzE1wjazlE1H/aD53F6NKvY+ZVE3UJol7vUthse5Gf/zACgWlxjVyJpAuVIjWzKYapZgLCW7do0nuQUhCNnDjd7lngH+37sYsGWEJ95GhgJiJZukkMqd19Pp8Pu3dvR3f3RZSXV2HixKkprVMuUrSG49KwCuC62NwrcNI1ivMRv04Edy9Yux2Ow2/DlHceDrwN4HLgTwW1TvqRjnS+Jq6epEvaLlIC+MrUX+Hv+76Lqv95AflZyv4uqdHfyJVu1MhsqqVxLPjYqd4x0YzWQBiCHkwkcoCQQ6pNNVK7+9RqgJKLEhNpufCFR/gNRyf29hnH+YhXJ0JOLtjD9ei84te48NEY9FT8CIAy39pEDVfAwG5OFGvEU9r4w5+xjozG+L76OmC1IzDzYQDq+AmriS/k1cShSA15RzGRFTVIOZ2b6GifLHfOx/nlSmRtf0rRScCxuRnWox3RnyMOB0OBnJxcXLvwZux5bR+OrmtGz3SvpF2xL+TFq0dfwdHueozJLMN3ah5LKd3EuP0IDstLertUG6BSgVvv8189Cn5zL6Z/+5c4+s4v0LTudVTf9kNNnpfvrRohctIt7VZYF1MTfp1o/nTAaoX/Oz8Cc9PXwE6aAzReDnxKvmOB0Ytx/sFO7Fm5K3pym/lA9YDunRCqb2vR+ANc6lX410PIHZ2LgspM4Ij6pQc58OvplqefxfbWT3Chr0UDhyJjZzZTPokmmtGSMwSt1odOqgbkYCWV8RTuh94X9mHDubUpPbfU2TU1G6CSwa33mZtPITNreMpaw3JIJjyiVF1GTfgzqszU6TG7dX6KW8p3LNHMMv8zeuHQoQEvIB8z9zkuO05gQS24ozEH60bq7p4lNHc5d/giPD75JxjmKILD7ECIVccO0eiZTQVBNBESh6AFVD1SRqIG5GCCO3PYeaIT8380V5bmq1YfejHkmAArhVvvy80oRm9bs2ZG0Ikw8hwtX4w/aZ1IwncsUco+TrHHmT4Bea1SjdzgduK1N1RzuOKiRGBeK4TmLvkORfOGL1LlubilQwQPYePPZxpKEEaTICr1A6umrRogTQNyMMHf2Xc2XZSl+arVh16MSAPUggXXgWWh7RwZp943zJyP4TnjUtYaVoKRMySp1ImSfceEUvZcuCe33LKpaTthqJV14cIPbmg5LCiwoBRFAvNaITJ3yXUoGp05VpWn4mY2j20tg6tlmKEs1lKuiSZCam5crP0/VaR2iRodqbUnfk3GYrfI7vqMfOjvqnhQtQ+9GNw5sglZXaj68zjV0lL8+u4jOVbknO2v95l8AVSPvxkT77lfleeSxQCaoxWqc/FJ9h0TS9lzR4yuevhZHPygBY3rP8RVd0rvnVDC3OGLcEXhLBTYC1XLupROL4mpb4+96wfodP5A9ZnjVEdjNEVg7jLOoSjkg92shs7v5a7gEXOAYVP8hpizjiA5iMppKpD6gVV70F1ql6jRkaMCFBFHcOTacXxDI6Z+Y7LklI92H3phInNk5osNyP/bQlUfm79x2zjFgX/aKK5J6/N5sXNnLbq7L6KkZCRmzboaJpN6iRmuGpTR5mjFZu74c6Phm5bGpXiTfceEZpYjI0YjFv0vtOxZh1DfBZgYwOeuiJ4whOYO1cRpccJpcUazLneWK99QCQW3MLSbOTYSQnOXXIei9efWYE7RfDxc9X3Vn1tIEEaoSTLPlp4ZbMlBVM6FXeoHVu1Bd6ldooYnwemF/2H5dvW/YM6PZmHzq1vQfqwNZ396FoWVhZJSPun60MegkeoKf+PGjhsL08Qu0Xof15Zr69b1aG1tQUnJCNXWw8VIGZJEM3eC+qL8IJrkOyY0s9y2ZR0YmxNte19CoNeBPX+agMKKURh/c41mc4dCm/6NE5vSknURmjmWcsqXchs1kHogSrQeobnLezEL91Y+osmaY9YlMGetXWdwcqSnc2WmpRKlCYV2DQEV0npSNSAHAmKnF+6H5c/vvonN67YgZ2kGAkwA1VdXY23Ne5iUN0VS1+e9lY+o+qGXchHgy+CphdDGzfqi+GlDqTemHIyUIUmoJiZBXzTZd0xI+u38JfH4yIjRqPIzqL7tDrVfWgz8Tf/xGl9asy58pJzyk91GTHULkBeApR6IEq0nUk9PN2KCMFqk66UiK38VubD3VufGONQH+gIxzhQnXU1xH1guYkV+LT3fBhpipxduR62v0gPWA3hWBjGqcDSmLZ+W9g8QFyl2Q2LuFmogp6lBqTemHPguGfp27CboiHVmKPY2jaTsly+/DdXV/TVOvni8liNGUXidxJvdW2PM0N9oTK9snhQXkUS3EfJT5SLL6kviJIOWziepItZkle4mSS6ytt+RC/upjrMxqbBDtQdinCmOjDyAULZ4mlBs1yBltzZUEDu9cE9cDaF6fPPxu6MBQ2q9R6kOpRhS0oFaCb7Lre9GvDHnzl2EvXt34NixI6iqmqjKWvjwT29iJwGuXCBXsUZNxJw4AO30RfUSj+dmc+7NVDfrIhspLiIJbsNV3bLnDceoBbfF3FXKd4+LpDq9hs4nqZKoyUpKk6QW1z5ZQTRyYc/NtcSkwkbNGIWJ8yZHO6ZuLr8Fd1XdJ/o4YjVTuR+EwUyi2pPQh0Vql62mDjsSvnRaCb7Lre+m6o2ZCMkpNYHSSEevD69sasTR1l5MG52neC1icNXEzOb9aGrKxvbtf0NJyUjMvHkJTDL0RcW0Y/lNWzOvvEYX8Xgj1aIluYgkuE2QlxKPU92SGfAkvTcqOZ9otTnk1nabHa14c/zbuGGk+CZaq2ufvCB66cKekeGMpsJyc3NRWDgMJhODVY+uQc7IbBxbfxyf/ffOhKLMghd9A+589EKs9iR04mrtOy/5FJbMYUdRc4OML52agu+A/PpuSt6YSZCTSeGfBMTkAtWGK9fZ3V2Bgwfn4LrrrsfWretx4WInSmTUuQS9UaeWxDVtXWi7oIt4vJFq0VJO+Yluk9RPVWbAk/LeqJGZ0HJzyM3orNu1HiFT4k20Vu5isoJo5MLefGh/XCps/PgJWPjEfHz23zvh6ejDNU8txJZ/34jqnb+D/fDnMRdkfiBwu92o++NBtJ2bgRLzBcwKs6p7vg0WuCcux/5f4aFzh/GXm16UcQqL16H88s9MtGFhesXNMKeYUhf60ilxblEyhpJsMyDmjalEx1lOJoV/EpBiyKwOl2fu2tpawLIHUm6uEvNGTWfTlhjcU4p9f6vuWtpSXEQS3YabEu9uPoQv3Dk4tPNU9GQnN+BJmWTgrycwZQp2bt0g6/uo6eaQk9G5b9jd+Oc5TyW5gzYavCl9uvmpsLbGNnRu6MWUOybBkWNHYWUBbJlWjLh4ANbOI3EXZH7qbdGZmzCyrQwLF5rwyZYinF//OYap7Pk2WIicuMwXG5B/cGHMv0mBXxO7UPcZWj+/GLUJC237HJYUU+pCF4GGA18A6JeB27ZtI4qLSyUrFSkZQ0mlvq5Yx1liJiUUZrF/xylk5jqgZz85t7kqklGSi9DMnhqPqxQ5I3npQKyblZ/qFOt4jfip7n7xIXQ6R2HDsK+Aq/Mm1+qLn+kSqhXy19x8/Ijs76PWm0M5M9hi/QD8rues4aNkrSGlIMpPhU26chq+rDsYVe6ouaUG657agKm4APPUKXEXZKGLvt/tR6jPB9MnG2B66X+r7vk2qFAwa8l32LlQb4ptWGCcMNkv6VHKTKkLXSiUOLcoOdHIrq+rMb96KaXm83nhbm/DmZON8O7+NGbHHklv3eENoqfInNrzqESi5iqpXplCM3vpbNoSZQAoRclJdUb8VLtmPohTLS6MPd8T+3sFIydSa4XpzDDwxyAfHP8d/LnpT3FiCnLq3kLuYkJew3KDaEoSLZFU2Fe+cgcWLboBecPzYpwpbJlWLHxiPhh/H1wdgUuvIPEF2ZZpw4bntiG3YhiKP35Hdc+3wYQSzWG+w06fazT8PR2YeNczCPb1oLutSfGYAxclzi3JxlC4Avy7Xt8VM3bFuntl1dfV0HGOuKK0bN8KR2srKm+5Fe3trWhtbYneJpLeeqPYiUNFzpSfSw0SNVdJcQUSE0bXomkrFYyupR35LEwozZZ8n2QuQakg1a8znWNh/DHINxp/LzgWmay2e/zDlfjkyeux4/kVuHh2epy7mBpew6oL0HO/WGGLHYh8gZJckLluCEfXHlN7WYMKZbOWsQ47ZkduTMNCjyMcY4+lNKWuxLklcqJZvPhGhMNhHDt2JOb33Av9udYz8Pb24brrlqK9vRV9gKzNgBrzqxFXlJKf/gQdV14Jx9x5cTv2ZBdBviGzllRUVMNms2Hz5n+AYcwxzVVxjisCGqViM3uJHldruIbYWnbn+nze2E0bG5b9GFoExNSQ5qaT7PuoJnyHqRLnCEHHKcbth+Vcr+DcNX+2tq+9Lc5dLNL1HDlENK17XfZaJZ/HpaZ3uKLM1cXjUNq0LukFWUyFghBurlFz1jJuhm/uVTDZzkm3x0oCXwauou0jFK2bLalxJ9mJJqaxpdcMi9kaTTWxVVVg335L8mZAjfc0klJzu3txcNtG7NOxJigFseaq6O8F6p1cxGb2kj2uVvBr2lp256ZTNlJrEs0Oc1GSYeBqSUtBaAwyIqZw46FxWHRxEoB9AAB/VSE8yyvj6t/JZmsBCV3PEpB8EpVq+sw1Ha5++V/ATJqc9IJsSKsfHeEaG+/ZsyP6ZY2kBsNZIxHOGYtwRn+aKpVZy4hd3b9738DZcfnY89KD/TZhSx+UbY+ViIgMXG3tZkwptaOo7iXJ95VyoolYa2Xn58DP+qKppuyvf0OWV6Ya72mEdO7YtYSbHTq86miMKpkSH0dNvD0FvIn5SlFqYoQOZLXg+nVarXUIBisEb5fuDANfgSzys3v+yFh7wRklggpMUk6ZeeOmoud0gyKvYcl/ebF29kRILXYnUqGQ4x4zGHB+uRKNdXuBsrswd+412Lp1A3JycgW/rEpmLaM1hxkv4VcZL8G29J+wWAPB5ogMHMJB5L13vazGHSknmsiF/tN/bAW8LBZfeyP27t2B4ydPoCqFRgs15leNUhNUAj87dLHpInwuX9xMaCpoIRYupMmspZa23h3Ick92ieDODidy00lnhoE/Btnoaoj+fGXJ1fCZQsjlBE2hLl0pp8xI17MSIRBZ26dk6R0tMFqrupZE0lEXcx6MdrT2f1k9qn9ZhaQXEwlcK0VMeF6JDFfMhR4mgIUhApcUIQc1L4JawPfKnPXITJhMUMXHUQux8ETexFJLUXJQswNZ/8+CXTM3nVThj0EOdxTH/HxtzmJ8q2lWNGgK1b+TyU1GO4BH12NMzUL8fNERWC1fv/TbbwN4SNJaZQVRfjt7+dKx2nu4DYBWdVXgpKMyT13ExUsdrX19HlitVixceJ2q4wL8msNXvVPQ+vn7Ma3eagZRoYtczy1/VCTDxb3Q54/PAwp9ihWI1JAo06smqCZi2SE1NtFaeHsmqmmLKSspYTBkG4xMstl36+F2hIrao0FTqP6d6JQZCrP4/l93I+QsxH8ueRG/P/ZzWMwX0NHxFkymXBQUFEpeq+QgKtT8ky4PN6OZGmsB96Q2tf5hfNh1A9avXwWGMYFhGE2+rFzpRfNnh5IW4ZUgdJFTIsPl2NyM7KMdWJ7jBKY4+9P8i8oUrTFd+rUDFaGZ0FQzCVK1nqWSSJM5lVJUMrSQjSSkww+aQgpMkdlavtxk5Hve0mnBLdPnY5ijCGW5IZhMiF6LgBcASLsWSW4sEmr+4bchi6VluG3nqZBKq7oaLejphDti4WMyMS2wEwsWXAeGYRAIBLBhw2r4fD7VvqxxGrzuLsWt3okQbtyR1lovhHfBmNjmAk6aP9XPWypze2pRULACRUWLUVS0WNb7kC6EZkK5Q/rt7R/B4/mGpMcS0n9WC7GadqQBTa1SFH9W3uGQP/MbCrN4/J06vLnzlOL1DDX4TWN9y8YjMLlI0nvK/Z47LRn4+OxHaHKdwQXXDZeuRW4Ar0lei+STqFh6h5+W4Y9kzKksVCalhtRa1QdaCzr3pGb75Keod1wNX+1mZGfngmEYTJo0Ddu2bYTb3ZvSF5YPv+aw1GdHmcJWbylwL3JSW+sFEUnzK5HuU0OiLJVGOE2ddVSCXyMdf/N4mM1HU8okyHXcUQPuKbrxV7sxw3b5NKp1s6LQZ+LcrFLKeigg0jTGLb8snVws6T3lfs9Pu5uxtfnPuKviWzD55iEEJ4LBK2C1Nkpei+y+bH76Zl3TqJi0zHGOvuK2T/4B5/EnlUmpQZpYMp+B1oLOTUflB1tww01fAwB89tknyMnJS0k2LxH3Vj6CO0c9EG24GDb2LHpa/5pWz0chGS45xKX51ZDuU0gqjXBauUuoifAmOjVBb6F6F1/mTc3+Cn4pyleahZ6v1qStWVHoM5Eu157BBP+AVj7hSvzX5hPRoCn3PfWFvGi+uB9fu3IprhvTjtysu9HV9QosljoAV0hel6R0buSI/Ne9R2PSN0cuzIlLy3CD19TuNQjkjFMkpQZcPqrzSZQCS6dElZrw01FKZPOSwW24uHC8BDnjFmDPSw/1z4umwfORL0Eo1lovBj/NLyTdl/Y0qZURnFlLTOppbT0JBqvgdj+EUKjyUiYh9bQkX+YtIuumBnGlqKVVKfyNFCDwmZCjVjTQSlNawc0utre34mDjyZjyi9B7ypX987SfjXm8i/6LCLNhrD+3Bo9s/QCNXTbk5z8Mls0C8KDkdSU9nrX3+vDyhuM42tqLJVMvxOyYtxwbG5eW+eeR90WD14reWmT0eGA6uwkAEHxjMdbmfUfURoe70zCbLQiHQ6ioqBa0z0qWAjOECLYKVFbWoLZ2U0qyeUJwMwkZyx7AqNnfuNRwwWDM9Y9iWNW/q7BqqShrreen+fkdwLmrbwVzX1fa06RyG+EUpbV1RGkmgYsWYy/RdQqcotPdrKjk+QZaaUor+NnFyaPycc2w4aLlFyFxee7EQYmzFBNHVOPuOY/1/0MY6OgAGMaEwkLp5cOkQfRYa2/0iBwMB2PSN9+dxuJe919ibn+I4zW6aYcfI0tLUeXoQM7Gh7Gr+BFcN1/8gxD5sIwaNRanTjVh0qRpOHToS0H7rGQpsMHSgs6XzVOSyhXaeOgx+6sWkTR/dGPwUA487gcRPFGKnI0Pw3PDD5Bnek52mlTp3J7cRjg1g1E6kTqkLwUtxl4SoaWurtrPN9BKU1ohV+AimeyfWvO5SdO5V1cURo/IF7rLkqZvuMHLZy9Cj7kw2pEZdAxL+EGIfFj6+jwwm83Izy/Ewt5RKPvbqThx4WQpMD1FsNWEK5tXWVmN/Hzp80t8hNwaEgn/c11SlEq9aUHfsvEITsm4XGIIrIPb+l1OB/AIXdKkchvhlKa19cMeJ+itBL7MW4REKblU0VJXV+3n06M0pcV7rhS5cppqiMtLQdaWZnrZBhQUfIyurpdhtuzHrlYPXvni3phGAKH5qYDDieb7zsGzbWPCXUTkw+L19sFisSI7OxebslvRPnU8akorY5oAkqXABsPAO8CRzVOF2EaQ4MWV2PP2DaLC/1oMqauNUEYiWlcOQpc0qfxGOOMpxqQbobEXu9kumpJTKoqRSrOiEsSeT8ppKFlpSu2mrGRp0HQRCrN48t06FKILUzPakJOTJym7GHlPj55VLi4vBVlWaAdPLYrumNu9PrxzNDumEcCxuRmFrx3AkgO5uPNsBW4Mj4+OY0jZRURuc9VVCxAKBfHxx38HrCYwGba4JgCposnEZfiNIHkjOxIK/0uxw5KDY3OzoGWRMsQzElI+I1qsSawRbqCj5Vwjd+zl0V0P4o3G/u5qIb/Hjl4fnvvoMI62SjeM55Puv5GS50tWmlK7KUsNj02lRP7GDRfcAIC5c69BR0cbTCbEZRfF7AOViMv7PzwM7398Ev1f4CPxU6+sk2gwbIvumEPBPnyvxhPTCJCovV9KjTJyG5YNw+FwoqioGC0t51CUkQ9L09mYorxQPWaoidXLRaj2Jib8H0HNmqkWOsiJMhJSanZDSZtZCVqrOYnJvEVSctO//UscfecXaFr3Oi5ecd+QGg+JZPc2bf4HPDY3/t71PkrqSqMnTrWbsoTe8+rbfqjSq5FGZFylrbMTDocTBQXDkJWVg1GjxmL8+AmSHkOJuLx1aRVwUyXYi174/7Qf5qvFsx2ST6Iv3DoZd8waHrUwern+/4AxMdFGgHnDFyVs74+pUZoYjH37z/DPn43g88/F3Wbv3h3w+Xw4d+4MKiurMazbLFCUj6/HJFKxIVKrvalqlp7S+EdiEp82JdTsNFjTYEQvNSe+E4f7fJOBzKzTQ6Q0lTUzBweK6vDTmc/HnDidFmfUazN6LVaA0HuebiJ/Y4uZgc/vS2nELyL7t/jFLZj52G9gz5HeT2KymmFyWBHccRrmmSPADMsQva2skyhfK/cPx/4fDnXVxehfirVyc2uUoTWrEPp8Hyx/eAvBe1YgfNNSMDNnidYxmU9OSivKayhWL2SOzR/RMT7yam9amKWrPVqgRofoUNBmlkKiOqMaak6J8Pl82L17O7q7L6K8vCo61pbMiWMokejEqaYWsZHec6czA17vBdVG/JLBHQFkfQ8jfGQC7I9elfA+sqIAVyvXBOBQV12c/qWUVm720CGYJk0GM74KpnEVYOsTfzGFzHXFalmRC2JgynA5Ly0p/EHf1tYWVR/fiGhhlq7+aIHyDlEtxh2U6kWnGzXqjGJIeS8aGuoB9Ne+Ghrq0dHRBqA/JVcy6+a0ioAYFbETp9paxEZ6z1mWRWFhERYsuA4sC1XU2sSIjABevPgWgC0IHV4CU2kWTEmM3WWdRLmzXKfcJwGwcfqX3FZuNuBH8Kknwe78DMySZbA8/Wz/A3ncMNkvLcxhB9ub+IsrZK4rVsvSav5rKM5qJTJLT5V0jxZIQe01KdHv1QutZOikvhc9Pd1RD12uvKWYE4ccBkcWqR+hE2cqWsSJ+keSvedynHv4Xq6T75mI/3fsvyR3EptMJly82IHa2hMYP74m6YifWEZDCpFO/5ycpwCMANt6H5jRBUnvJzsSRP6Id1c8iGtKro/7PbeVO7z+Y7AH6uLStnBmgO3p6b+Dzw9TVgoXMJHUrVYXab2d7AcL6R4tkIKqazKAfm8qaJKulfFeSJW3jBnnqCzDLTWPJV3GYFH8ERsDSua9KUSqDXVyzRL4Y3KnpjXigkm6feZ/3j497t8SBXFuRmPbto2CQj3i9Hf6+/1LkJf3Mpiy98D2JNeClrUdk5I24LZyi6VtTTUTwB6uR/hYA9imRpgmJq5FibXWC6VuhVK/asAf0Tl8+CDpWaaAEcc/1FyTkH7vYEJsnEAIOe9FZWUNuro6k9a+UhnnGCxZJLExoJRIsaFOSLAlEfwxuauy5kmyzxQjmf2eUEZDKpdHAMcDmAVzdQ8sM5PPxyb9NNk2NcF6pP/YPwnAQ/nX4zesxLSBSNqWWbIM4dqtCN5/F5gly/pPp5fgNzdwW+unlOZgx8pd0dTAnKvHRFO3zi9XImv7U2j7nqv/SF+7KaUjvRj8EZ2OjrZBsbsl1IWv35uz+na4lr+j86r0Qc57IVXeMpVxjsGSRUrlxJmI1Brq5Dv3cMfkSseXgjEzKcs7ism9RtLTOTkeeBgXTK1Zsrt5IyOALtcrAL4AIC1uJA2i/oVj4J17+dh/1U0rMDtf4gsXSduabDZYX3w57uZCs2jcWo3rjCsmNdBit2JERUFc3UXZkV4YvhJTefl4HD9+dMDvbvVES/srveD6wuZsfBg9i3+l95J0Q857EZG3PHz4QMLaVyoau2qaUYj2eeiEnPokn1T6R1IxS+B6uR5dewzts88p6CQWDuKR9PS41k5s2/MJPnLvh9kir5s30umfl/cQgCsh1ckleTrXak55jk5u2lZoFo07E5ZXlheTGrCHwmCzzNG6SwQlR3oxIuM3y5bfjo8uFGNLk3dAWq0ZCS3tr/QinDUS4ZyxHP3eYp1XJA+hdC0b8CPw+GNxc93JkPNeROQtly+/DdXViQX4xTR2xVDTjILb5xFevQrhvXuiv/P5fKit3YTVq99FfX1d3M9qkyy1mYxU+kfkKsU1bW3Grl/vgdVpgYkBzofOKOokFrXfu5SeZuvPY2JeGRYsiu3mlWKJyAZM6HjEjXM17ej6VycAae+NpJoov/YoVf6LWbIMpomTBNO2QkgZorZl2rD+J5uQOzoHmXdNhdX7p7i6i1iTglJvSe4YgNXTIksMmYiHOzKltv2VEnwhb1RU5IW6Z9Hlvyj7Mfi+sAOZRIFDCmq+F6mMc6hpRsHt8/CPr8ArnX+Kfk6+OLIbwOUxnbq6fTE/R8Z21IJfn/T7u2QF7VT6R+QKtvDH5Jqrjymq6yYK4iZPAM6zHjQEW2MMO6RuNiKfc9sf34bnvffh27FD0pok5SC5x3458l9iaVsl8FMD8y7G110qF70W58Ept6tMiJgxAFP3oLBa05N0219JhS8qkqiDcDCNT4gh2CDI2RArGSuQSyrjHKqaUXD6PD6bakGbuTf6Odnp3o7r85dEM2BdXZ0oLR0VN7ajHrGpzRMnPgJQJrmMJTQ6KOU55Qi2RMbkjn+4Emc+fRkT3i/CN7/1UsqC9onEVSwnu+EoyMcNN10dc59ktpkRop/zqipYxlfC/2Ud7FdfHXc7PpKCKPfYL3eeTKiGkOzCY0EQzo4DWL16d8yXsutkF3bta4lR0BGquwR88U0KUt/IRHDHAPy2Yni2dmP93zbBNsaCOY9Vy3osoh81lVbUQk7zymAZn0hIkrluLXoQxFC7uUY2nD6Pqw+GMKP0qmgmhbGYYzJgmZlZksZ2UoVfn3S5AoKztnqjriuMeBAXT09LbIbifM5NDkdS/YLo80q6EefYL1e3UigVlEz9pxRtYBGbBnnh1sl4+OaqOAUdobqLsAdnYv9RuVgaXXCGM3D9M9cheIpF1/HBkbpLJ2orraiFHC3SwTI+kRBnBli/v/+/Bea6tehBUJNUa7pCcPs87A2NKKieGf2c3DRuecyYTnFxiaSxnVThpzadTqemQTtV0uUKI5aeFq2j8uF8zlmfT7J+gaQgqmSOTigVlOzCM73EigljR8Z9KSOpga/+ejnm/2gu7FmXT5LcuotQk4LkN1IiwYlFqtqEDSUitekdXT9Qb+5NZaQ2r+hhmJxukjUIShVK0AulNV0u/D6PT0f3Rj8nRZbhqK6eFJWoy8zMjvlZ7VMhvz45btxNmgbtVEmXObZYnJLaDBX9nDc0IHjsOGzTVBpxUYxAKijZ3JYWX0ohGzClqGUTptRgeCDBrU1/c3QuvjHKBqB/MxTpZkxHbS0RYsowQqg5PmFUEs11A/1CCfweBCORrKYrB26fx6neZrxZ93T0c+Lp9cSM6RQUDMPOnbVJx3ZSx47OC89FZfUKrzyO6vkTMaxoeMJZ23TDd4XRyhxbDKkmFZHPuf++O5Hxta9JqocCcrVzOYIG0u8UPyua7MKj5EspVm9Vw+0jQsQ5nd/kNOEW+XVRrX0alaJ200ii2nQ6a2uJkNO8oub4hFFJ1iAoVShBN2RqdUtF8HNyY+zn5MYbtVWu4srqbXppM1zD2nH4yEFJQVvq5l2pT3O6XWGE+nCkNENFPucMY0J+ofTRH8lBlC9oEAkkSRdWMwHhrVtiUkHJLjxKvpTijR7yusqSoZZNmFbC32qhfmATL/KLiZCnGznNK3wRDiXjEwMVqUIJuqGGVrcAujc5oV9Wb9TskQgHw2D6zJhVdTWGVcW//3xhk2+O/i7+sK1D0uZdqXG9EnPsVBDVbNcIaUFUgai2UCqowu9PeOFR8qVMV6NH6fQSnNp5Bhuf3YLCioKUbcK09mlUitqBLZHiidFra0KoOj4xQIn0IBgVoY38YEJKWYk/trXq5EcoL7pe2uY9BZ9mvnvLlfc/LtuJJ/4xZsDEJG9oTZa+T/VxxZDUWKREVDtyRLZt3w3LT/s74yIXnq985Q4sWnQDHA5nzH3kqJfErVVho4dUQYZETU6DCbUDW6Iiv1QRcoKQg1zRl2QYzSs2UlYK+cM4uvaY4G34wibFuTZZUxZyfZq5aeYze86h5WCr5NcTeX9TfoxL6XvH5maYOwOwfXIsxnNaydqEkHRM00tUOxWdSimNHmK1ADUEGfSCWwte7C3DiA5z9Hdyaxhc1G4aSVSbNnxtzQCovYseCqgp+mI0r1ipZSWlwiZydXa5aWY50wvc9zfVx4ik770LxiD0eyswswyslYmmoVN+XBEknUS7bv0YnSt2ouealwEgbaLaqbSmJ6u3cqX7+Mi1+TES3FrwZxln0bisGO6vT4j58PCRIt8YCWzqten316Y7OtbC5XoBLHu5PiU830twUXsXTciAU9YyCnxZvfE3jxfNpsnVHOaSis4uV6JV0vSCwPsr+zHAGVVpPg62+QRsTHFcGjqVxxVD0kk0nNWvLhHuPgEgfaLaqbSmJ2v0SNzII9/mRy2kNmqJwa0FsxYGTIYNti/ETQOkdgWno2kk4kRRVASUlMhzotCbdEreAervogcjWkkxcsta/N4QvTIEkbJSBLFsmpyxLSFSMa6XO70g9P6mMgER04dzwxI4mNFw89LQakxWRJDVdZN2Ue0UWtNtNhuu95fDejYXOAugvj4mnclv5OGmjINLliH49JOQY/NjFPizt8OcubA0nRH1CpTaFax108hATqED8d3Lo88AIxoD0d8rSaWLodZ8MjBw7egSjWdoJcWYqKzFzRBs/o9PMHLWSJROldfFqgZiI2RiY1tSN+9ydXbF0syJxiT576/1jeWo7XlG9gQEN31vPdyO0JH2mDS0WpMVEYytlJ1EbkwM74Ix6HlgWtJ0JhCbMnbm/wMF2d+UbPNjJCK14IirTMeeowlrGHLlG7ViIKfQgfju5ZYxFsmfvVSR0kgilYFoR5eoJANo16GfqKxVvnicQRTMhOVN7618BK/N+0v0f8lE+5UilGZOVk/mv7/uG1fGPYZchNLQQmtTgqGFPlNuTZfRks1NGXueHw3HGQfyy5ULMqRKqupF/Fqw82IAoZICDVeqFvql0NUgrnuZDV3y35U+DiAHtXfRcsT2jUKyLEoyRbRUSVbWUjNDkCqpmGZrAT/NjHAQ2e8lHpPkv7+2krFY9GNlphRCaei4tSnE0CdRJa3pkluyuSljxoGLb88WbHpJB0I7bKnt9HzPxDxLlmyvQD1QW9M43QiN5cgdB+AT9AaxY+UurHp0Dfa+tg9smI3+Tu1dtByxfaOQLIvCz8qo7fXLLWvFeM9++SyueGyqKhmCVEmmE3v8w5X45MnrseP5FfC0n03buuSMSapZNlSi+y4VY59EFbSmJ2vJjtQCglu1UTNJBf4OW047PX/o36/JCtVHC03jdCI0liN3HIBPovqa2rtowJh2dEpIpxRjJB3+Xffj2LtlD3YuqUUhM0JxhiBVEo2QqWtJJg+9xiTTgaGDqBKktmQbSc0kpumJDaesEiWnO1FpV7BS+F96V+cK7P79rgEzBynUvcx8eVL2OACXZB24SrVMuSjt2jQiyTr01Xz/IunwDG8mvtxWB+a3GbBVKs8QpI64vCnXksyeNxyjFtyW9NFS0ksXgO/73D7v/2KXAcwm1MBQQVTNNnGpLdnJHCr0wnp+p2g7PZD4QmAEo2jpgTz2S3/ysyZDdDlKRah7OZVxAD6J6mtKtUy5yBHbNypCn7VEUoxqvn9REYP2j7D1pjV4ZvoLhj3NRyzJpn/7lzj6zi/QtO51VN/2Q9Hbqykswa93Hj3Tb2+pt9mEGhiqJqrmILnUXHgkZWzdsh1sd7cqxr1qYGk/CPuJNchd+88A+tMfXBJ1IKvVnejY3IzsV/dF/xeRzZJCMuN1MYzT5Zg6atRhEnbgWplLzUvic8BSSXfXppq8cOtk3D1njOBnLaGQiIrvH6BMxCCd8C3J3OebxG+skrAE/+8QqXfqZeTOF6JQQ8LRUEFU6QU0pshf9yy6/Bcl31dN41418NZ8M7FKVIILgVpG0XJGhfgoCeRqqomkCzX1VJu2NmPXr/fA6rSIduAqbV4aTPA/az1+JByBAdR7/4TS4UYlb9xU9JxuuGxJVlImelsleukREo0i6WE2wZ1Jb2//CL6zM1Q5aeuazhVSe1HSJs53Kthwbi1uLxPfSR3/cCXOfPoB7LlFuCJUDnMalf8B8ZRspE4ZRmKVqMiFgC+ooJpRdAruDRGkjhkIvQfea8aqpiaSDtTWU5XiEKS0eWkwwf+stQdsSYVE1Hr/BlI6XI4lmRqNQIlGkfQwco8RoggWwfS33Sn1nPDRNYgKeVUWFAyLu4Byg920b70o2lEmZ+aN36kWWr8bFselk4SAOpIWiiRSajOJ2r3FLgRqdieKBepkCAVy5vCWuL8j/z044g2g9dd7VJuD1BwFNoFiSOnATUXLdDDBnaeekd0Z81krDF3AfXMnCqqSRYws1Hr/jOApKhUTw6DmjickWZLxG4Gk6qXz59wjjZKhYBBbt26I1q0rKsanzWwiosrlx37cUj4CY+2PYdTBH4AdZhLtOZGDrulcfl78+J5jcWksbrArnnE9vB3is01yZt64nWpdjfthLSxJqI6kSa1OYW1G7ELAnxlVYhSd6o6dH8i7zjYK/x1570Hp3NGqzkFqjRppL6mwAT8Cjz8G//zZMH15aEDMAWsBP02YbNMoVKph3H5J75+SvoCBTDhrJMI5YxHO6E93S9FLT5S+dbm6YurW3d3daTObiGQoHyj/L3zQMAJrTh6G6cszMB87JNpzIgddT6L8vHjJqBHo/NIdk8Y6uf5VWW3ZUmfe+J1qbf4Aig43Jhx10UKRJNWTHiDeBSrVKFpKq3+qO3b+mEFmxzG4RP6O3PfAkWlTfQ5SS9I5/8YNBq57VsAy7/+BwcDsaFQCP02YbKRFyMii7x5pVmCRTEmwoxdZHx3H+q4vUVTvGtAjGXKQI3yQKH1rdzhgMV+uW3O72n0+b8wpVS3DgAiRDOWI3G349/l1+PuRsQjdcy2C/koEGitknbSF0PUkyld7cWY744yuI8Fu4l3PINjXg6Z1r4s+npwiP79T7UKhOak6kpqapRGETnr8BqmOnjZBBRulXaBSGoek7tj58I3XTX1don/HgVzfS6dNoKCr0RCEr1jE/6w5HM7YO6RgZBHlUqYk+FkjThX4MGXRPDQ01KOjoy16EzHrsaFGIiUpq8Um2uzI765uOXs6mnFRY1IikqH86ASLI50m3DVlDcJZxXBbfyDrpC2GridRKSbM/GDnOnVY9PHkFPnzxk3F+Z1rLneqjSyH9V9+BODSCe3VfdHb+msKcYRBVLN0Ro4dY870Apduo2RYW+ikx2+Qev+Ld1DeNkH92UkJjUNy3RvESPR3HMj1vbTaBCoJBkMQtVTJTJ4ACtpZnJyVgWrOSEZhYdGAdyFKBy/cOhmHDu3Hef/lunXTxzswrbn/elNi8+B8USh6SjV/8Xk04xK8ZwXCNy1VPL9f27oFf216B3cxj6Pccn3039WQGExbEBXqxJXiVRkX7ErLRZ9DTpE/UaeaUMNPKYNox6Sz2Ir6CRZMHT8BwzacwYUyG3JlvyP9CKVk+Q1S2aOzsOCJeZp4SCpJJwshJqCf6O+ohjiB3qTFJtBpHInKgYRSVTLLyW70ZgEdPlfcSIaY9RgRC79u7RmRgZ5rp4Bx+ZH50QH0mgPR7ur8ui8AmT7SieBnKF0eF77Ys0u19HHagqhQJ25hYVGc2gu/TmerLsfFWTdLasuWQ8JONYETmgOI1uo+++wT5OTkoeiEFydy++AOdiMXqSkCCZ30oioolxqk7iy/XzOHCDVTqYmMvhNtWtQ67Q52tJKoTNU5SApaGWQnI6aj//7/DWttakYWQH+mhBldgK6u0wIjGcZ3IYqY3gOA262P6X1c3bq6Jup2tH+UH1bGhrnzr8HevTvQmOlApYoZF36G8hrmeowIjlRN0S1tQVRIoUIofcs/BQauGIGafGlt2WqS6ITmdGbA2+GCpSmM+uGdGBmUZzkm1HLPh98gpaYTOxc1U6mJGgvktNcPdKSOZMmFL1EZuGIKXq3/uSJD7UQbH6lIMchesOA6bN26AatWnUFp6ShVgqmY7nOc0HpPGzJSNLIA+jMl/iILqkfFl56MYj0mhh7pZqG/i1CzY+Qay16VDabDfbm72uFIyUdaDH6Gsq2tBfX1B1Tzm01bEJWsUKFgwF9NIic0+65zcR2slTNrcGbtTrSbzfDbWNmDwtwuS6GcPz/9cPiTo3jlYAdmTxyOMpVnJ5WkUvnBYk75yMsC+kMUIaeMc5/+XZWgync12nruY1niIkIk2vgIlWCAWFHyZEE4oibU1tYv4XnllXNQV7dPUz3nVITWE9G3bDy8rm407qyNKz0Z3YVISbpZTaF+ISLX2IoJ5ejY3Rk9pVYUFoM9/L6qGRfuwSXr+hvhuWqman6zaevOFfJdFMMIkmaRE5pQB2sg4EeFswSOimKwLJCdnYfa2k1Yvfpd1NfXJX3sZF2W3PTDd2t/gP88ewJnATRtPan67GSqHb5y5neHEvz5Y193h2bv09zhi/D45J9gmKMoZUPtRB2V3BJMpCOVr84UCcITSrMFHz+iJlRfXweGYVBaOkKV3X8i5HT0SyUykrF8+W2orr4cKL3epQgGq5Cf/zDC4awY6zFj0J9udrmegcnUK6t7WOjax51VltI5m8gbN3KN5XdXZyxdjqw5QPH6maqZgnAPLqZ/rMGwU6dU85tNWxCNdOIuWHAdWBYJFSqMMPIQHe0QEESwWKzwX+zBkZYTqKysxoUL5wHEXmwScqnL0ufz4bPFC/GPTFtM8OWKgn977C+waNwETCjNRtWS8dHRH73hB4uskVV6L8kQ8C/gp7b8WbP3SWtD7bgSTHtLnCi5VIPsOXMWIhwOY9Wq9xTpOUtBltC6YvpdiDo61sLlegEsa6xmL0Wm9wLXPrka44lMRcTG5yzuZuQUH+j/75+qYwbCPbj4R45EztlzqvnNpi2IRjpxpShUGGHkgXtC45+Mc3JykXXvPNR880ZUV0+S70jgzADr96OhoR6mcBhz+gKiwTfZRUoLfD5f0pO1Frv9CANNJeb4hyvxyZPXY8fzK8CyiLmA+1ydmr1PgLYOIvwSzIjG12WrM0W6Ms+fPwOTyYQxY8pU2f0nQo7Q+mDH4fgABQX3gGHOwmqtQzBYIev+/Guf3FnlREpvglkwldxj4uCMhzly8mALBFRRdAPSGETF0iERuBeivrYzhpI0S3YylutIYKqZAPZwPVwt55Db1ISCmklptQNKhlAaj0+i3X7EoipVlLjHpBt+WtuRVxRzAbdl52t2KtLaQYRfgilpWZvQnk+IiATlyZMnYDKZUFZWocruPxHDp1+Lkksd/eFQULCjn3u98bSnnmJX071HC4TSzXJee9y1L4VZZTmuTJrJaF46uACAKRDAmAmTxcU5ZGIIKzT+hahrqlWxH6OaJDsZy6n3Av1dlqaJk2D/7FP0TpyI4NRpabMDkoLQyZrvC6jpbl9lv0ct4ae1Ry28I+YCPmrB1zV7n7i180d3PYg3GlMX0hba+PBLMOduek+2OlOk3rV06a0oKBiGTz/dosruPxGRTvDFL27BzMd+A3tObNZLrXp+pD4cCNlF6376E5tuvlD3hazXHnft4wQjqZ2zcpTeHIfflr1Rk0Lk4KL2eBiQZsUiMTsxtbvp1CZZB2sy5aWIi0DMKMKLL6O6x5V2O6AIiWbH+Cdrl8cbFZaOdGDKsVVKBbVFILSCr8HcvOGPMaM8bDiM3rPHNHmftHYQ4YuhZI+cJGrPJzZuEkGqnnMqyB0pUuV6w0k7nth8Ah6o6/CkFXJfO//aJ3dWuWlrc1TpTYorE989Zr3jdlxY9TfFM8b88TA1mpWij63aI0lArMjMra/5Pd14/8/fTclYWyuSdbAmq/dyZfx8YR82nFsLQF6zldIUKRe+Oa3H842Y3/NP1l1eNq4DM9luXylGaC6TQrImFjXfJyWm86nAL8FEOjPdK36M810/0vS5pZLKqVKNej437TipaJP6Dk8aIfe18699kSya1GBUOr1ElisT3z3GbcqOauq2trZIfJXxRMbDbNt3q9asFCGtJ9HyxeMwavbIOPk67oWod1QRmOYD+NkVb6c8+5ZuuI4EXCI7ZGduIR69/6fxowghEw6+cRih1nrkVuUhb4k80YZUSTY7xj9Zz6gai+vyC9M6/2mE5jIpyJGlVIpc03m1STbfrAepnCrl6HGLwXfvGbblTry17j7VFcXURui1yznJ82eVkyHFG1eIwOjFqL+jARYVRRG0Iu01UaEiM7e+ltvuQXXFIoz6zI2s1iDM+88ZvkNTyMWBu0MumXEj7D2euFGEroMuZJlycP0z16GnwR3T/h1Bm9NH4tkxOZ3UWpGqe0y6kdLEohZqzIUqwYguMlJOVvxuc7n1fKFGHL57j+ua/1bd4UkL+K+dsdgMO+8dmTEWcn4xEmkPokJFZu6FyBQOo2bJt/H3yjoczTqHq2Z+RVKHZrpTXRHEUqP8hpODtgtxowhSjL7FUsFKSDY7lqyTOh0otXlLF1qntbloPRealBRdZOQO6MtBykwov9vcPHqK5I2PWLqYm3asb1+MnX86BavTIqnupyf8TZ89r0iVOWYtupQjM8ZqiSJoRVqDaNPWZuz69Z64Dxv/QrS7rw7vnv4f3FnxACoOWyV1aKodbBJ98WNVOHYCbH9qNCfnOZhMnv7bcHbI7t52HFz9X/hZ+0O45YP8mPnHZO3fWpw+lM6OEfLgdzYrQcu50KSk0JkJQPaAvhyknCr53eYXuzqTbnwiRtF7P/4f+EIs9v/mh4JBJjB6MbIf+6usup+e8K+1bCiguD7MV7FSSiSzl5X1FiyWbiBogqe9D/VrDxuw+znNQVRKkZk7+3ZVxkyEmtskyf+pHWwSffG5DVKnd7tx7LN/jkuNcnfI7aVZyO724T8K/oD7Jv0X3pz95WUZLZH278jJ+od7vo3fHP0vfHDyb6qdPuRKlUXED37V6sN3v2gzdGpdLlobKnf0+qKdzUrRei40GamOCWiZBpaSTpc7xw1cFs4fUVSEkKcLJUu+GxdkIp+d0ZVLsOS5Jnz118sNoygmFcXqThLEEeTMpXIzeyNG/BssluHYsm0d2ACLeV+LVz0yAmmt1EopMvNta+aVT8EDmVclfWwh+zAlCH7xLzVRxDZIMQiZliEUGhHj4sBtOBnrsiGz5iu4e8El0+8NJ+CfbMOJuhbR9m/uyfrnB/4dq8+8j7srHlLp9NE/OyYVIX/VwUA6HC4SCbwnQmgsSo7pvBakPCagoZm4FHegysoa2aNkEeF8R1YO2OzhyBk7EWFOE9JgMeOW0xgnJEhvzvpLtEvZeSB+TlnIkCFR4xK/6XHBgqfBsnnwu/2a+CmrgeHanbizb/ZPToLNMMMv8b58+zBFJPniR/w988cGUTnrMbh6fhnj4iA2R8mdfywNhqNG34UVBTEn84gxd2+gB53+DozNHBc9fdjNaW64MYizjtqIdSlzXUqUMqe8IMbZRqq3o1AnrtZzocmQ25kZRWcz8WRz3EJEmloaOtxwulph97tigsxgMeOWM+8ttJnO/yi2Szln9e1wLX8neh/53dPC/qxa+SmrgeGCKBc5Nl1CqS5FwSbJF/+yv+c+fP7OIsxa8TACgWnR1KjYDpk7/8g1+o57+ksn618f+SUA4KS7CY/uelDT00ciA+WBIn4gj/gvrPfMTarWd7iY4Jd8eolsogrshbp04vJRYuyslZm4VPiiEVK6zSNNLVff8T3s/UMrdr/4IHLH1HCCjPHNuKUgy+dXYDPNF0fgq1jxxUia1r2O6tt+KPoUYv6sWvkpq4Ghg2jfsuQF+oghsCnzBEK56qW6En3xY1U4GHhc16Cj43uSHlfO/GNt6xY09zbi7oqHcE3J9Sm9DjlE6kBCju9i4gdSDMaNStwX1nwkWt8RSk0BiTca0dsIpGIBwMR0ST69qF2eUILS1KWWajFSEJvjTkREON9itcF8xS0ouvEhTJ58RfT3Rjfj1gr+Zjqc1Z+aFVKxAuTP5Ar5s8pVPUo3htDOTRVu08bUghlR+7DX5v1F8WktkTKHXBWOmMflzD8mKrjr0UQSqQMJDTeLBX+xBiyjC3MD8V3KONuZVPyau9EQU1ER7xQ3J/V25DY67br4v/XrxOXATV1yO9CloqVaDB+1HIAiwvliTh9DtcNdbDMdGL1YsPwhdyZXqOlRyfU2HRj6JJoMoaYNuTqaYiSq/6SqwgFcPl0nK7jr0UTCHW7mO76LpdaFGrDMFbmapUS5KK1der1LYbPtRX5+fyresmYrrN1tovUdIPFGI4JQKjaiLet210Ds9MI98Z3s6cZrh59XrzyhiIGTupTSBOfz+bB793Z0d19EeXkVJk6cGnebZFq//M+O8cy4tUGukph8je34pkdHrnjZywgM6CDKb9qQ2wmmJ8kK7no0kUTqQHPnLsLevTtw7NgRVFVNBJAgtc5vwOpxJU2JqoE6s2mxX1hm+fcT1ncA4Y2GkLGCUCpWKFUV85o4J77tzVZdO3G5DKjUpYQmOK74wrZtG1FcXCqp2SgW4Q53NZvS0onUdcvpUwFk1lwHKAM6ncsnEpi++P1TONIdxPYjzdi9+1OwbFjvpcWhpam1VPgp10gdSJbjO28A39a1Vxs/QC4qGffGimbsQyhjRIz4tdeRG6eCJaSiImSsICSKkHw+97Ic43em2vCXG+epVp6QgtjM7EBLXfKNpPkIWf2pgdqiA+lCzroHipJYOhlUQTQSmHKvexAhmDAueF6x+r9WKB5yVvr8Al+cZHUgIfgD+Bn4QhM/QC5qGfeKuQpF6jtCtU2hjQZfvrGTbY+rZ/fXv5dh3Y9P4PTR38HlegEsG5sWSybHyCeiqrNq1d8UbxYTOfvIEefQUuJPKskcgFIRX0iKShs7rYnrw5C5br3kVY3MoAqikcBUPK4GNrsT/vONuqj/8084QjJVmppaJ0PkixOpAyVzfOd+kV4atQ/BmqpoA5bnX9bJNm6Wi1rGvcm0i4VUsMQ2Glz5xr3Oz2LMsn+773lJIt9yT3xSmpykkrh5KNbYmR/8uWgp8SeVZHU7vtWfGj6+am3stERIB1juurXQ8h7oDOiaaIRI00br5/3qG+jthC8YwqkRc5Cng/o/94QjZtIrpeAupBDiW1wWcxspTRJ8uF+cRHVLsXEOvhDAmh/Mxe1lKwEAkbOQWMt7KkTGmKaNzsPdc8YknU2TA3eIO2dkNnas3JWwtinWcMKdY5t9dBHuveVyPbvh/VfQIWHgXG6zipQmJ+kINw8JGsrbxIfdEyl9pYtkdbtUxBeSwbdGE2pK0xuhPgzHrgdlrdto88tGYFCdRKM6mqv/CFM4iPlXzdVF/V+KO4sU9w/vgjHoeWAa3F+fIOpkw3eo6OhoS7o+qSc5sZOOFJ1isZZ3Kad0LkLas3zjXiWBmqtdvPe1zyXVNvmIGStEX7Pk+re0E18kJdfwm3+Bp/2MKlZRYqlk2ScPXqNZsLtLtZSzVJLV7bSw+uNbo2mRgVGK0OdQ7rrlOgkZIb2vNYPiJBohEpjYwwdw/vxZOHMLpTfIqIwqMlUSOg2FmiSS7aylnuTETjpKhAD4p/R1vgDGzBiBu+eMEbx9Iu1ZsUAdIdkpnT/EnTMiGzMfnBHV6GyztEhSwSqdXiIq3wgoN4Hmjm2NvuYb0ZRc3do/wurvxdylX4vrppaLWOew7JMHT+mrK+AXFfDQi1TEF5KRTHTACAh9DpOtWygTceDifsnyqkY0clebQXES5XeZptIgozZi7ixySdZpmEqTBPck52XM+OXJPwo2CiQyxU3VkitySnebWARYFk0ub8LbzykvwH1zy2A2mSQ/R4Rkp3ShIW5ubfNz666Y2uYbjcKp78jcsJiLh5L6N7+O1XlkZzQlF2hphClvhLxuahHEmofknjz4jWbWqdNVTDkbn2QbOz1J9DkUWzc/E/Fe8//IEoExopG72gz4TzS/yzQUZvFvHx7BtNGVuPsr4sPSSklUr1RTpipZp2EqDhURAqMX4+Ov/g8unF8XrW+uO7UaY9ZVoe1IOzKuNsNaFD83qlSn2JZpw84n14MtdKKgokDy/eSS7JQuJJrBrW1edeQa3HfLtxSvQ/7A+WX4dSxrVl5Ui/TIX/8TwWPbsTkQQm5uvsLNorizjxxjB77En/3qefBs2ygo4EGkl8jncO9/3oN8azfK7v2PpPfhZyJOupvkzS9r6OBjFAZ2EOV0mToP/A4dvT68sqkRR1t7MW10nqZPnUgZRSy9J6VRiE+yTkOlTRL8L0nXuS4Mu5Ru3fT2OmTk2uNOOkrVlFiWxeIfL8C+P3yB0Se6AI3+VnJP6VppdCoZOOcLePt7L0ZTcoUTroL11GEs/sodqqxTCLkbJr7S19FD+0UFPIj0YmIYTLrhq5h/8SWYgh60CfRh8OGXbp6Z/oI8CUqdHXzSwYAOovwu01S9G1MiQb1STBYwFV/OZJ2GqThUcIn7klz5AoqvLO2vC562wzqtPy3OPekoUVPiByompJ1LvdxTerLaph7w61jtB7fHpuQS+D+qgdINE3+u1u3rwcv1P5fc7as1UgwFBg28Q4dUlFhM6u3gkw4GdBDlt5XfWP8tzFn+TlQGMIJWUlyy7cFS8OVM5mSjRpOE0Jdk1aNrkDciFwuvmw/GrN5FhR+ozo/MhBSZ+sgYkxzkntKVaCJrBd80OW/8DIBlU0oNp4JS+cmKimp0dLRFN2KtuRdwoS3WJ/X2Mu0ECvjNZRPG18S4Dp3+xjcM1/ikFVJH27jOTJ7rF+LNG8+nXLrR28EnHQzoICqly1RLKa5k9Uoh9PTlFOq0c/m749J1NsYm6N2XSNxfaqqaH6jWvK9do4HSU7oR4NdTx910P+w5hQNGi5Q/V9sX7MPs4rlpmzPk6+SOrDsAJ6dbNHvRNUOm8UnqLCu3o9Z69+0oLxuN9WxqmYiUjdwHEAP6E5O0rTzF9IVUmFY3gsMy4gb0TYx4J2kqgVct+CIJG86thSfojknXTQ5Px9V118bVBZOJ+6eSqgZSO2FKJZVTuhLzaS1wfnIKszquBSr6A5F/Xw98iwfeZiBCun1S+c1lgd2fIIPTLepsaoJnWN6QaHySOtrG7ai1lVfjCWYJzPMu/52GVApcAoYPolKszcTas6WmL1KFcftxwRdMqk4Ucx+ZVkJqIjTzx0/Xebu92PXZ3ri6YDLXmVRS1WKkameXSuMWF6Xm01qQ6ubEyAiVD9SyMOTDby6DxwOTM6P/lw47TiI0ZBqfJM+yJumo5YqwDPYUuBQMvX0Q0nqUwgu3Tsbdc8aoprEqRt+y8ci9dYKoOpGQWgfXlDvdSJn5E5t5lKK6k2ymVQqp/s0BaQpPiVBqPi1XjUkSVgawW2Db36J4cyJGOlVlhLp9lfzNk8HXyTVlxLoOMVabfOeiAU7SWVaeM5MpKwuhMIvH36nDmztPqSw3OfAxdBDlnn66Gvcja2SVrPunS4qLO6DPVScSEuNOJkmm9QWNL5Lg8/lQW7sJq1e/i/r6upjbckUspLjOqJGqlvs3j3Gl6D6vMOBctiIzmXpj7MCkIOYKEyFV1xU1NieJSKdoPLfbNyJgofR7nohIc9mCBdeBZQHblKkxYhAV5eN1F2YxGnzBjJ5xVTHSm4lEWIYihg6iSj031dRYTYSYOlEqah1aXtCETgFiqj78hiwpqjtqpKoT/c35AV/oBMMNOMmCFv/kGPCPl2VFxieZZnKqrita19HTqSpzb+UjUY/UiE+qlt66fJ3crNu+AdPESdFuUcfsOZKci4YSzJJlMe/RkZETUF6UgQml2QAg6Kk7lDH0OVyp5mgELaW4Eg7oS1Dr4I/fSHHBSLV+JDTzN6lncryqT35+XEOWFNUdoZnWZE0IfIeWRH9zfqdl0NMUV6flBpzTx48krN3wdXyn/tNBlFSujtOPlUMizeRU02Ca19F1VpVR63suhFBz2WDvFlUKv6N2DvrlNyOjg0KeukMZQ59EdfXclIiQ/moUgdoCF8HxmyQXNCX1I6FTgJCqj5DHoBTXGaFUdaLTl5BDS6K/Ob/T0t3VEXeC4QacZEGLf3Ls6bxBsvm0GIk0k1NNg2leR0/yOVWClPLEQPieE5cxgja5kTD0SVSJ5mgE7uAws2QZLE8/q+oaxQb0CwpWwPxif8DoPfVL+PlqHWLjN0lkspJ2ycpESNVHTW/ERIFMSGEq0d+cH/CzrPa4EwzjuHwa5gYtsfEF7skxf1yJqH6sFJLJBkbSYHI7QZMJbihFS1UZKS4eanzPifQh5qk7VDH0SVTK6ScZ6WyaiBAdlWh5Hee/MxndS/8nTq1DzFGeX9TnX9DUrh/xGy8KC4tUbchKdPoScmhJ9Dfnd1pmjKyOO8FwT8NSajdque0ASbIS0DYNxu2elAu/Bqamqgy/PBGo2x9Xp5b6PU+1MYsgtMTQJ1E1kFJjTBWxel90VGLYswi/UgyXaz1YNi/mvmKnvWQyWUL1IyWyhkKqPpFLkxreiKmevoTgy/iNnrME9t5W0RNMsqCltuB8MtlAvgSeWmkwpcYLmqrK8MoTrvNnERw7KqUZQ5pPVBex65fYv2spjDKQGfRBVMumCfEvdf+ohNe7BNnZLyEj40243Y/G3JerHmLb/BjWZj2A7tXv9ptHJ7ig8bVUs/LzFckaJlL1UaMhS83TFz/gFxQWoSCBO0qyoJUOwXm+AtL8+coUkIQucGk1XpALrzxhyy9IecaQ5hPVRez6RZsVeQz+T6GGVjxiX+pgsArB4GgAzkujEg1x9+Wqh+zPuhkw2zB37ixs27YRxcWlomLpMfWjkeMx1VSrmqwhX6x74sSpih9TjdOX2M44Wb07We1Ga8F5pQpIQhKEQhe4OeUjYronjQS/3mr9zvfg6elMSWZPSo2bkI7Y9Ys2K/IwdE1UDZLVGJUgVu9zOD5AQcE9YJizsFrrEAxWiD5GYPRitFbcGzdmIvp6OPWjhfPLYR0+Pq6umipiM6NKiASyRHN4EYUpMcQ6fPWod8tBiQISNwC3t38Ej+cbALS7wHGFNdSEX29tys+JqVMfPnoQL9f/HN/ZcS9eqHsWXf6Loo9F84nqInb9IjEFeQz6IMr9EjfPr8S2vz3Vr27TrlxaTOxL7fUulTUqIdc8OoLasobcEZKMzEz8/fA7ki5uWiMWONIpEpAaqSsgiQVgLS5wWjodReqttu27Yfnpc3Hp/aau41FTBF/Yhw3n1oo+Fs0nqovY9Ys2K/IY9EE08iXufvUltDm9qupzin+p7XC5nkdHx1q4XC+AZROnkPldp8nMoyOoLWvIDebuvl70+TySLm5aIxo4dBYJSEYwWKVAAUk4ACu9wPlC3tiTn7ctOmqVDvgzhnMnL8bjk3+CYY6ipNZoNJ+oLtzrlzkcRuEvfg7//NkY/oc/0GZFBkMm2a32fCWQvN4ntcYo1zw6gmRXBolwZ0ZtZjsWjrxW0sVNa0Q7fDWsd6uBw/EBMjL+jAsXfg6PZxvOnSvE6dOfSrKOEqurJzqNSeme5NvhbfniZ7hHQ6cjPmJ1ainWaDSfqC7c61dFw3FknTwFyx/eQu49K5A/oQYbuvuzTw5HRv8o0hC2O0vEkHlXtNDnTFbvk1pj5Ot7yjWPVkvWkDszCgCji8cmdHxJF2KBQ8t6txpE0vrDh38XPp8NublPSdbMFaurKz2NzR2+KObkZ+qs19TpSAp8UwQiPXCvX9VhE5jJU6KlkRHdLuTk5OHGG2/BxYvtknWehyKD/iQa6XDMzvfBP6pEE31OMfgydZ2d7YKnzFTMo7WAP0Jy0F8X5/uoNvn534TF0u92srZxLMY7fgmn2YlXj76Co931GJNZhofKvyd44k82U6s//Wn9trYW1NcfwIIFwyQ3A3m9S2Gz7UV+/sMIBKZF6+pKT2N8U+y7Z7+AzgXDkxo1a4WQKYLdrI9V4JCGVxpxhtm0degGvUHseW0f2o60Y8T0Elx5/wyYGFPyOxqEQR1EuR2O9tIv0LXl/15Wtykt1/z5U20Y0gtuMD/V24w361K7uEkVyDeZ3DCbW/Fv2ybg3vInsa7p12jOXYsCW2FMyvGT9g24fX58zU5TkQAVSW00w65IgjARXFPskUWzEIZ6JQG5CJkiPFz1/bSugUBcacSal5+2cSK+EcTIWSNROnXgmM8P6iDK7XDMWjgcF49dm1Z9TiFd2oFCqhc3rkB+y9518HacRcawkYK7TYvtBEwm4N+v7gbYlchtsiLEhjB3+CJcUTgLBfZC3euxaqCmapNSxE5+WjodJeLeykdwb+Ujcf8uNCNLaAd/nvf8P30tbZ/Z8sXjMGr2SISDYUELQaMzqIMoXzloxv1jUX3HlrQ9e6oNQ0ZA7OKWDLEGLqHd5ugZl/8+YftPMWuECyPN34pLOSZqNhkIGGk0YyCc/JSKVBDy4ZdGfJMmgzl/Nm2f2UQWgkZnUAdRKcpBWiKkSzvYiTRwTf/2L3H0nV+gad3rqL7th4K7zcjfp7Z1J8K2HswvLYbZ21975aYctarHpuu0o5VmbiqkujlKJ9wMUjhcDJfraQqkGsMvjVT4/Wn7zIbCLH78bh2uuLkCmXtbcHTtMUy4pVqz51ObQR1EL48Y/Cc8nm04f34YOjvrVJGzk4JRGobSSSKDZf5u0+H4AHbnG9iw34afzsmE03QFekI+tPadl9RsokSmkHva8Xod2LXrc3R3v6ua3CEXGs2QS3LtaUJb0vWZ7ej14a3XP8fIk11gb65UxQgi3QzqEZfIiEFR0XcQCDjgcDymmpydEVFih6UWiQyW+bZjXu9SnHJl4qVr2nDW3YVvfVKLNxp/F5NyfHTXg3ijUXh+UYlMIfe009z8JoCwqnKHSigoWIGiosUoKlosS+VosKBMpIIwAkFvEDtW7sKqR9dg72v7wIZZwdsdu+BG3qRhyGcYON87Ak9nHxo/aUp4H6MxqE+ikQ7Hzz77BDk5eZg8eSwyMw+KjpoMZJTaYamFmMGysO2YHbmht+DuAsrMwH9fdflxpKQcpY4QCXP5tNPVtQ7Dh59BQcFNKTyOuhitHhgKs/jxewcwbXReQn1jNYlkkLq6XobVWodAYFJanpdQD6kdt3PKC/rNE7p9GOEPY+KRDsx6ZOaA6tId5EG0n4E2apIKRrHDigjk8+3JUrEdS+aTquTvyq2XO52fweXyGOLzYaR6oF4bM7EZWWLgkErHbV5ZHhbcUjPgunQHdTo3QqratAOJOeUFuG9uGcwmYw4pR2zHvvrr5Zj/o7mwZyUODFJE0ZX8XbmKQFOnHkB7e7Zmnw824Efg8cfgnz8bweefi/s5ltRF6/n4fD7U1m7C6tXvor6+Tvb9IxuzCaXZKa8hNeRpTxPGxJZpw/qfbJLVcZvKffRmSARRrpwdy2LQpXKNjuxabTgoSRRdyd+V67Tj9eagunqqZp8PvmVb6P/9WtTCTUk9kB80lVrbKd2YxYndS3QCklpPI4wNvwdCq/vozZAIokq1aYnU6ej14bmPDuNoa7zDipiHZca+FxG6JIqeCGV/18unHZ/vBzh+/KRmn484y7bP94lauMnxouUjFDST+dRq2YzGFbuX4gTk2NyM7Ff3ofU/P4X3SAduvK4CZ/acQ8vBVtXXRmhL09Zm7Pr1HlidFkkdty/cOhnzfWFZ9zEKQ6ImOhRHTYyCWK02UbrWcfhtMH3tsJ9YA6BfFN21/J2426n1d7Vn2lE/6lC/Vq+vDt/xP4Y8W76iEZoY+JZtbjeYkpLLP3Ms3JTUA/mNVgAS1oy1rnnKVZ7yLhgD77xRGO3yo+y9w+idUQrT+uMDpjZGXCaVHohU7mMEhkQQHUpIscNKJ9Huu/c5htmcdK2Q/VbXrR8D4WDaRNH59mAbzq3F7WV3xpzstm3biOLi0tRSvXzLtowMsH7/5Z9jLNxS18zlN1oVFRXj3LnTorVe7gYnxPrxcv3Po6L/36np30goQbbylJUBwMC2/xTC04ux7hfbB1RtjLhMpAciGfyNqpT7GI0hkc4ljEWydG04ayTCOWMRzhgOQHtRdL49WOTEJDRCkwpxlm3TZ2hi4cZvtGIYU8KaMbfmeZ7dI5p6feHWySmPt8i1OTN5ArA0dcE/uWjA1cYI+Sit2xsBCqJE2nEcfluSh2W6RNGdFicK7IVx3qlqjUYxS5bBNHFSVJfU/J3vx/ysloUbv9GqoGCY5JpxielKwY2EEoTE7pNhOdmNI31+7PzTlwOuNjYUUNrxzUetjaqeUDqXSDvpTtdKQUirVy0XHiHLNi0s3PhazaNGjcWoUdJ0hy0mR8xGQg3R/1TE7plWN0ZMKUbjnrMDrjY2FFCtxHGJwTDDT0GUSAvcWm04q99flOthmUxYQUtE7cEGgAsPv6akpNFKbdF/IbH7ZKL/jNsP+9i8AVkbGwooUwmLZyDbRUagIEqohmNzM6xHO6I/+2sK4VtcJnr7SLpWirCClvBPTLOGXQ1vyIujXYeQE8zFrCNXYUrlFYYcjVLjZPDCrZNxqrcZ/ytFE3apSJE07FsWf+qUavJOqAsb8CP41JNgd34GZskyWJ5+VvWT40DYqCaDaqKEangXjEHPA9Pg/voEsFYG/ukSdC8lCitoyb2Vj+C1eX+J/m98dnV/k82Ml+DMyQQ7qb9Bx4ioVVMSE/1Xc46UK2mYk/McTCZP0vtwTd6LZ1wPb8dZxesgpMEXCQnv3aO6+ttgmOGnkyihHpwRBf/kIrD5jqR34XbqCo276IHc+UY9UetkIJR6lTpHKnRiEUa+xZmYyTuhDonS63EiIfUHESgfp+rJcTDM8NNJlFCVyIhCYMpwSbeX2qmbTsS6dY2IlrrQUrVzhU4sQqQiaRgxeZ941zMI9vWgad3rKb0WIh5uer29/SN4PN+IvQFfJKS3d1CcHNWGgiihKpaT3QgVZYDNlOY80nXrx+hcsRM917wMAIbo1AXkzzfqhRL94OMfrsQnT16PHc+vgKc9Pk0qVTtX6MQiRCqShnEm762nVR2xGMokTa8740VBIifH5ctvi5Y4EhsqDH4oiBKqwrS6ESqR7rqRbmEFKaQy36gX/JNBx7a/JAyMEVStNQqcWITgiv6Hw1mSJA35Ju99xRMBDOzhfOOQ2DEoTiRERBREaiZisEI1UUJVGLcfwWF5su+XLmEFKaQy36gX3JpS24FaNFwKjC1718HbcVa0k1XVWiNf1jBLbBOVWNJQSKuYb/LOFI1FnoojFkMZrqduIDADbOA4dqzchbYj7RgxvQQz7lwKU+3WOFEQX8iLV4++EpWI/MEBwMbPRKgkIDIQoCBKqIrQiMJAQ6jJZiAgJzBGao3Tv/1LHH3nF2ha9zqqb/thSs9rqpmA8NYtsmUM+UEzFOpv4IqM6+QPK8Rf29/C0dH1GFOzEN+peQxNh44N+OF8o+BwfICMjD+jq+tlWK11OLhhFjxtHlzz1EJs/o9PMHLWSJQKiILwtabPtZ3EOPuoSw8qnokYrFA6lyAGCXKacPi1Rvf5JtHbJtPO5csaSpUxTGbd9sXpeD1fLRuphhr89ProebdiwRPzYMu0wsSYou45/DEnvtZ0wGFJYKgw+KGTKEEMEuKacE4dFr1t3ripOL9zTbTWmFlanvLz8mUN+ek+MUeYZNZtYx3leLzyJzGjRoNhON84xKfXbZnAqkfXRN1zhMac+O48RdOuBfvHv6puqDBQoJMoQQwS+E04mSVlorcdPv1alMy6GXteegjhUBBlN96n2jqkmnHzZ1zz8wtiTpkZ9oy4USMasdAWlmVj3HPExpy43evDvnaPJoYKAwU6iRLEIIHfhJMoMJoYBjV3PIGaO55QfR1SxSr4uqkmkynulCmk5zvQh/ONStPWZrQevIApd0yKuucI+QHzu9f9JhZ2DQwVBgp0EiWIQUIkMC5+cQtmPvYb2HNSO6UJzf3JmQWUKlaRzLqtx9ozYEaNBgOl00vg6/Vj47NbYMuwibrniElEDlXoJEoQRAzcub/gPSsQvmkp2Nbz8f82YyZ+/N4BTBudF9d4lMwR5viHK3Hy881wT7gZpsw8VFVNjLNu+9Px3w6YUaPBgCPXEeOeE/EO7e6+CI9nFIAcAPHd6z6fF1u3bkB390WUlIzErFlXw2QaOuczCqIEQcQgpEDEtrTE/Fv7wSP41dnMaMMJd1ylcNRwvOkRd4SJCD1c/f1foWXvOuSVj0WBgMD/QB01Gixwu6c/fLcOfX1uwdudPt2MYDCI665biq1b16O1tQUlJSPSuVRdGTrbBYIgpCGkQMT7t2NuNqbhhHvBPd90Bjm+XNF0H3eetatxP7JGVqXtpRHS4XZP3zGuD4tHCd8uNzcPFosFTqcTDGOGxTK0zmZD69USBJEcAQUitqcn5t/mZIcwb25ZtOGEe8HNzsrFt0Y9ivHjJwg+vJjQg9TRGCI9SHUIysjIgsfjxqpV7yI3NxeFhcPSvFJ9oZMoQRAxCGmmJtNRlWPJJib0kGg0JugNYsfKXVj16BrsfW0f2DCrzYsnokgVtmhuPg6r1YrFi29EOBzGsWNH0rxSfaEgShBEDEIKRMlUieQoCYnNs/KVcLijMad2nI5K0p3Zcw4tB1s1ee1Go6BgBYqKFqOoaHGcQLzWSHUIYhgzGMYMs9kChmHg9w+tLmpK5xIEEQNfgUjs37jIURISm2flK+HcWX5/9D7li8dh1OyRCAfDMZJ0gxmu3yfL5oJlpdkLKoHbIDZy5Bi0tV3A4cMHMH58jaiwRUVFNTo62rB58z+Qm5uPysoazddpJEwsyybMi3R09CJMqROCIBLgcnVj585a+P0+TJ36Ja68shYA4HY/AI/nbtH7Ob9ciaztT0UdfHbt/Sl+523AXZUP45qS6+Nuv+rRNcgZkY2FT84HYx7ciTSL5QDy83+AYHA0wuFiuFxPg2XzNH3OAwe+QFdXJyZNmoZt2zZiwYLrYjZEQ6FuzTAmFBZK1/8d3J9CgiDSQsSS7ZZbbsaMGdvR0fEW2ts/gsfzDdH7mC82IHPnz6I/nzm/Fa/3HcVX25oExRX4knSDn8R+n1rA1zPu7GyP+b1UScehBKVzCYJQDbP5BEwmFrm5T3FOTwJpyHAQ2RsfgXfCnXAe+B0QDqL2wEsIZmbg78PL8fddD8aIKwhJ0g12+H6fFkuDrPsf/3Alznz6Aey5RZj2rRdFvWW5CDWIhcJsVFTj6zOlSToOJegkShCEikg7PWXsexGhvEr4xi2P/vyIaTjeGPMtrNq/Fq/N+0uMOpFUSbrBhMPxAQoK7gHDnIXVWodgsELyfSOCFrOf+COKZ1wPb8dZSffjN4i5Q2Y899FhHG3t9wiVKuk4lKCTKEEQKRP0BrHntX1oO9KOEdNLcOX9MySdnhyH3wbT1w77iTUAgIx9vwRMTPTnnNW3w7X8ncu350nSDQW83qWw2fYiP/9hBALT4PHcJfm+cgzauSfWsm/8W0yDWFc4A+VFgDd4+cSZTNJxqEEnUYIgUoY/ehLoekPS6anr1o/RuWIneq55GQBw8bZ1MT/3LP5Vml6Bken3++zoWAuX6wWwrPRmF6kG7fwTa9DVHmMCcP3UMtw3twxmkwlAvIMLmQLQSZQgCAXwR096Om/AsGBD0tNTOKu/PhfuPgEACBVNj/mZzSzWfvGDCDbgR/CpJ8Hu/AzMkmUw12RLMmjnn1gnL7gNo2fkiT4P18GFTAH6oSBKEIQibJk2rHp0DXJH5yB/XAlcrucl3zcwenF0vIX7s2NzM6xHO6L/7q8phG9xmZrLHlTwnXeGlT+E81xBi9JywfuJSTCKQaYA8VA6lyAIRWgxeuJdMAY9D0yD++sTwFoZ+KeXqPK4A42IHdnq1e+ivr5O9HZ8551cvx0ls27GnpceQjgUFDVoF5NgJKRDJ1GCIFJGs9ETKwOAgW3/KfgnF4HNd6jzuAMMrjvOtm0bUVxcKqwGxXPZgduNmnufQM0dTyR8/LxxU3F+55qkJ9YXbp0s+O8EnUQJglCAlqMnJk8AlqYuBKYMV+0xBxrJxA+iODPA+v39/33JeUcKw6dfK+nESohDJ1GCIFJGy9ETy8luhIoywGZqrxlrVKS645hqJiC8dYuoy44YJoZBzR3JT6yEOHQSJQjCkDCtboRKpI91DEakuuMwS5aBnTAB7ntux6czMvFz22p0+S+mebVDEwqiBEGkleMfrsQnT16PHc+vgKf9rKhXKOP2g82267xafZFqR2ay2bD9R0vxwq+uxZQX3yVd2zRC6VyCINIGd7i/Ze86eDvOouWAPyrYsPk/PsHIWSNROrUEfcsGv7Qfl4KCFTCbWwBcdr+xWKxobGxIakcG9Puxkq5t+qEgShBE2hCSoyuozpPtFZqKuLqREfMOjbjjSIHvx/rE+a8j++N90d/TrK02UDqXIIi0ISZHZ8u0Yf1PNiF3dA4KKhL7U6Yqrm5kuO43OTnPwWTypPQ4EV3bOysegHnxRJq1TQMURAmCSBtiw/1yBBu4p9muxv3IGlmVjqVrjHLv0DhdWyYA2C2w7W8Z0rO2WkNBlCCItJE3bip6uHJ0JWVo2tqMXb/eA6vTIijY4PN5sXXrBqxa9Tfs3v0pAhLF1QcSwWAV3O6HEApVXnK/OSX7Mbi6to/uehBvNP6OZm3TANVECYJIG8OnX4uLx7/AnpceQvaoKpTdeB9YNhOndp7Bxme3oLCiIE6w4fTpZgSDQVx33VJs3boejKNAkrj6QMLh+AAZGX9GV9fLsFrrEAhMkv0YQrq2lsPtQ37WVmsoiBIEkTbEhvsTCTbk5ubBYrHA6XSCYczILR2HszvfTypVN5BQ4h2aCJq11R4KogRBGJqMjCx4PG6sWvUucnNzUXHV9QidORRzmh342GW530iFcfsRHJan+uMSlzGxLMsmukFHRy/C4YQ3IQiC0IxDh/bj/PmzmDVrLvbu3YHRo8tQVTVR72URgxSGMaGwUPrpnU6iBEEYDu4caObN3wfDmGE2W8AwDPx+n97LMwQ+nw+7d29Hd/dFlJdXYeLEqXovaUhC3bkEQRgK/hxosZOBzWbD5s3/AMOYUVlZo/cSDQHXJq2hoR4dHW06r2hoQidRgiAMhZCq0fyJeXovy3AI2aSJaesS2kEnUYIgDIWYqhERi1SbNEJbKIgSBGEoxFSNuDi/XImilTk6rE4bvL09+ORPv8Wqv72BXX/8LVg2nPQ+Um3SCG2hIEoQhKEQUjUCLgdO88UGZO78mb6LVJlTm9cj6Pdj8aQr0G5h0LJ9W9L7CNmk+UJevFz/c3xnx714oe5ZQU/RwbYB0RsKogRBGIrh069FyaybseelhxAOBVF2430xgTN74yPwTrhT51WqS87JU7BYrcicMBFmmGA+1Qyfz4fa2k1Yvfpd1NfXxd0nYpNWW7sZlZXVyM8vxPbWT3ChrwU/u+JFQU/RwbgB0RtqLCIIwlDEqRqFg8h+7zZ4J9wJ54HfIZRXCd+45XAe+J2+C1WRjB4X+oYXYNWqd5ETCCC/z4MjnO7bbds2ori4NKZxSMgmLaGnaDgY3YAMpvdOb+gkShCEocnY92I0cAKA/cQa5K79ZwBAzurb9VyaapwsHg5LMIjFi29E2AT0OT/HtdsWxXXfJsNpcaLAXhj1FJ03fBFCYRaPv1OHv/z93Zj3kVAHCqIEQRgax+G3YwKnv/hK9FzzMgCgZ/GvdFyZejAFhWA8HjCnzyDHcxplfasBIKXuW66naAZbguc+Ooyjrb2wtB8clBsQvaEgShCEoem69WN0rth5OXDe8BrCGf3WXmxmsY4rU4/KG5fCarFiy6G9uJr5EJ6a/pqv3O5bvqdofUsXyosyMKE0G96ab8a+j4NkA6I3VBMlCMLQhLNG9v9/9wkA/YEzkFmMtu+59FyWqtgzs7Dw/m8jY8//gblrDrzjvwocfh3V1ZMwbFgRtm/fIiik4At58erRV3C0ux5jMstQaB8W9RRdf24N5hTNx8Nzv4+n3j8I1paDcM6YmPeRUA4FUYIgBgSB0YsHVeAUwnH4bTB97bCfWAMAGL3tYawv+gHGj69Bfn5h3O253bi/OvIS8u0FeG3eXxI+x1B4H9MJBVGCIAgdCHqD2PPaPrQdaceI6SW48v4Z6Lr1YyAchPX8TuRsfBj2O/6G5QlOjAm7cYm0QDVRgiAIHTi14zQ8bR5c89RCnNlzDi0HWxHOGolwzljJNV+hblwivdBJlCAIQgfKF4/DqNkjEQ6GYWJMsDouNw7JSblGunHvqngQozPHCt7mhVsnq7JmIh4KogRBEDphy7Rh1aNrkDs6B5llTrxc//Nok9B3ah5Dni0/4f353bi+kA92sz1NqycASucSBEHoBsuyWPjEfIT8Ybz/yTsJJfuE2NKyPtqN++iuB/FGIykRpRs6iRIEQehA09ZmtB68gCl3TIKJASb0TMGSRctlNQndW/kI7q18JA2rJcSgkyhBEIQOlE4vga/Xj43PboEtw4ZJN02+3CTUcwTLq99EUdFiZGS8qfdSiQSYWJZlE92go6MX4XDCmxAEQRAqUNu6BX86/lt8e1o3Zuf8DiybC5a1AbDpvbQhA8OYUFiYJf32Gq6FIAiCkEikSeimUbOxYKQXjqx/RU7OczCZPHG3Pf7hSnzy5PXY8fwKeNrP6rBaIgIFUYIgiDQjFAQjTUIfn9mFf147HP+1byJMpt64dG7bgVq0fr4Bs5/4I4pnXA9vBwVRPaHGIoIgiDTCDYIte9fB23EWGcNG8pqE+gA4EQj8DhZLQ8z9Lx77HIzNiS9/8yPY84Zj+FW3oLZ2E7q7L6K8vAoTJ05NuoaCghUwm1sAAG73A/B47lb5VQ4d6CRKEASRBiK+nn/e1xoNgl2N+5E1sirmdg7HBygouAcMcxZWax2CwYqY3wf7XPD3dGDiXc8g2NeDzz/5EEC/eXdDQz06OtoSrsNkcoNhWtHR8Rba2z+Cx/MNdV/oEIOCKEEQhMZ09Pqivp7hoC8mCDatez3mtl7vUgSDVcjPfxjhcBY8nrtifm92ZCJrRAVyyyahoHo23L6ALPNus/kETCYWublPidZcCelQECUIgtCYYxfcUV9Pk8UaGwTPN/FubYfL9Tw6OtbC5XoBLBvbKZo3bip6Tjeg93wjXCcPwWGzyTTvtsPjuRUu1zOCNVdCHhRECYIgNGZOeQHum1sGs8kER15xTBDMLCmT9VjDp1+Lklk3Y89LDyEcCmLqvBtlmXcHg1Vwux9CKFSJQGAGLJZTCl4ZQY1FBEEQaSSzuCwaBLNHVaHsxvtk3d/EMKi54wnU3PEEAKCzsz2peTcXh+MDZGT8GV1dL8NqrUMgMCnVl0KAgihBEER6MZmiQbC/S/brAFLvkrVYrGhsbMDhwwdEzbu5uD1L8C//Y8Os8rfxwIL4mishDwqiBEEQOsDtkr2sTCSfnJxc3Hjjckm37ej14ZVNJ1B/Nh+TS78Jl2tMSs9JXIaCKEEQRJrg+npyu2TD4WK4XE9HA6ljczOsRzuit/XXFMK3uEzx80canLzB5OL2hDSosYggCEJDIvOhb+7kN/DEdskGWp6Iqhh1TjCj54FpcH99AlgrA//0ElXWwm1wItSBgihBEIRGcOdD+XC7ZLvPFsFsbr4s5ec6D9gtsO1vgX9yEdh8hw6rJ6RA6VyCIAiNEEqf+kJevHr0FYzL34Xbq9y42PUyCuyHcOGEDfUf9kv5jVpwG0yeACxNXXDfOTnBM/QT9Aax57V9aDvSjhHTS3Dl/TNgYui0mQ7oJEoQBKERQunT7a2f4EJfC+YX/DdOujIwouhfEOgz4eD7/hgVI8vJboSKMsBmJm84OrXjNDxtHlzz1EKc2XMOLQdbtXxZBAc6iRIEQaSRucMX4YrCWSiwF+L/HJiGsqxyXPF5Oxz5x6IqRq5Th8EUuhEqkeZrWb54HEbNHolwMAwTY4LVkVhwgdvgRCiDTqIEQRBpxGlxosBeiI/PfoRGVwPmDV8UJ+WXWVIGxu0Hm22X/Li2TBvW/2QTckfnoKAiX8NXQHChIEoQBJFmalu34N3mP+POigcwOnNsnJRf2Y33oW/ZeAQmJ1Yf4sKyLBY+MR8hfxhH1x7TcPUEF0rnEgRBaMyD5q048/4H2LGpCIX3/n9489TvccOIpZg97Gr4Qj7YzfYYKb8IUudFm7Y2o/XgBUy5YxJMDBDyJROhJ9TCxLIsm+gGHR29CIcT3oQgCIIQoe1ALRrefQlXfP9XaNm7DhuLWrDLdyD6+zlF8/Fw1feF7xwIA+EwGJcfGR8ehfu2CXHjLj6fDzs/24aLHR1gT1pRhFLM+tZM2LNSU0Aa6jCMCYWF0mrRAJ1ECYIgNOXisc+jJtz2vOG4b8HP8EhWnrQ7WxkADGz7T4nOizY01IOxMFh47fXYtm0jJiwYTwE0jVBNlCAIQkOCfa6EJtzJiMyLBqYMF/x9T0+3LFNuQl3oJEoQBKEhZkdmjAm369RhWfdPNi/qdGbEmXIf/3Alznz6Aey5RZj2rReRMWykGi+FEIBOogRBEBoiNL4iB6Y18bxoZWVNjCm3r/00Wj/fcFlCsOOswldAJIKCKEEQhIYIja/IIdm8aCDgR3X1JCxYcB1YFjC1NUVrsF2N+5E1skrhKyASQd25BEEQAxiXqxs7d9bC7/dh/PgahPa+j7YDtZj+7V/i6Du/QG75VFTf9kO9lzlgoO5cgiCIQYBUUXm+KffRg+sU1WAJeVA6lyAIwoCkKiqvtAZLyINOogRBEAZErqh8hOHTr8XF419gz0sPIXtUlewaLCEPqokSBEEYmFWPrkHOiGwsfHI+GDMlD7VGbk2U/iIEQRA6wgb8CDz+GHpvWIhtb72G1avfRX19Xf/vSFTe8FA6lyAIQkfC6z8Ge6AOTc/8O9jP92DOuCpsb6hH6BzgPuolUXmDQydRgiAIHWEPHYJp0mT0mBnke7zIbzyOzMxsWIpN8PX6sfHZLbBl2DD+5vF6L5UQgIIoQRCEnnjcMNntcDoz0JOfB5/bDa+3D7AAi348H1955UZcdf5vMN08H8Hnn9N7tQQPCqIEQRB64swA6/ejsrIG3bk52FRcALOZgdXa340bSfda/vAWwqtXIbx3DwDAF/Li5fqf4zs77sULdc+iy39Rz1cxZKEgShAEoSOmmglgD9fDd+I4Krd/ivklY8CyQGFhEYDL6V5mfBVM4yrA1h8EAGxv/QQX+lrwsytehC/sw4Zza/V8GUMWaiwiCILQEWbJMoRrt8L0039D8z+vQENnK8aPr0F+fmH/DS6lewEADjvY3l4AwNzhi3BF4SwU2AvhMDsQYkM6vYKhDQVRgiAIHTHZbLC++DIKAdwodANnBtienv7/9vlhyuqfYXRanHBanPj47EdodDXgzvL707VkggOlcwmCIAxMJN0bPtYAtqkRpomTo7/bemYTznzRjGUdX0HPyW4dVzl0oSBKEARhYJgly2CaOAnB++8Cs2QZmJmzAACnepux99AO5NsLMPvq+WhoqEdHR5vOqx16kOwfQRCEgXB+uRJZ259C2/dcCW/3p+O/hfeoBy6LC0ey6nFz1zJMr5qJ8eMnpGmlgxOS/SMIghigmC82IHPnzyTd9t7KRzB7xFzMy16EV2e9AUfYiWCQVI3SDQVRgiAIIxAOInvjI/BOuFPw10FvEDtW7sKqR9dg72v7wIZZVFbWoKurE+vXr4qZLSXSBwVRgiAIA5Cx70WE8irhG7dc8PdC/qKBgB/V1ZOwYMF1MbOlRPqgEReCIAgD4Dj8Npi+dthPrAEA5Ky+Ha7l70R/L+QvarFY0NjYgMOHD8TOlhJpgxqLCIIgDADTexYIB2E9vxM5Gx/G2X8+hB11R9DdfRElJSMxa9bVMJkY8hfVGGosIgiCGICEs0YinDMW4YzhAIBTHW4Eg0Fcd91StLe3orW1hfxFDQgFUYIgCAMRGL0Ybd9zITc3DxaLBU6nEwxjRtvBduz69R5YnRbyFzUQFEQJgiAMSEZGFjweN1atehd2ux2VM8eTv6gBocYigiAIA9LcfBxWqxVz5y7C3r07cPpCExb9eL7eyyJ40EmUIAjCgDCMGQxjhtlsAcMw8Pt9ei+JEICCKEEQhAGpqKiGzWbD5s3/AMOYUVlZo/eSCAFoxIUgCEIH2IAfwaeeBLvzMzBLlsHy9LN6L4kAjbgQBEEMCMLrPwZ7oA6WP7yF8OpVCO/do/eSiBSgIEoQBKED7KFDME2aDGZ8FUzjKsDWH9R7SUQKUBAlCILQA48bJru9/78ddrC9vfquh0gJCqIEQRBpxPnlShStzAGcGWD9/v5/9PlhypJehyOMAwVRgiCINMH1CzXVTAB7uB7hYw1gmxphmjhZ59URqUBBlCAIIh3w/EKZJcuQNQcoXj8TzJJlYGbO0nmBRCpQECUIgkgDfL9Qi7sZOcUH+v/7p8/puTRCARRECYIg0oDj8Nuwn1iD3LX/DADIe/fa6KmUGLhQECUIgkgDXbd+jM4VO9FzzcsAAP+oRdFTKTFwoSBKEASRBvh+ofZTm6On0pzVt+u5NEIBJPtHEASRZpjes0A4COv5ncjZ+DDa7zsGNrNY72URkC/7l9QKjWFMihZEEARB8MgZ1f//riYAgCm7BHSlNQZyY17SkyhBEARBEMJQTZQgCIIgUoSCKEEQBEGkCAVRgiAIgkgRCqIEQRAEkSIURAmCIAgiRSiIEgRBEESKUBAlCIIgiBShIEoQBEEQKfL/AzHCNXyR8wzfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import manifold\n",
    "'''X是特征，不包含target;X_tsne是已经降维之后的特征'''\n",
    "tsne = manifold.TSNE(n_components=2, init='pca', random_state=501)\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "print(\"Org data dimension is {} ,Embedded data dimension is {}\".format(X.shape[-1], X_tsne.shape[-1]))\n",
    "      \n",
    " # '''嵌入空间可视化'''\n",
    "x_min, x_max = X_tsne.min(0), X_tsne.max(0)\n",
    "X_norm = (X_tsne - x_min) / (x_max - x_min)  # 归一化\n",
    "plt.figure(figsize=(8, 8))\n",
    "for i in range(X_norm.shape[0]):\n",
    "    plt.text(X_norm[i, 0], X_norm[i, 1], str(y[i]), color=plt.cm.Set1(y[i]), \n",
    "             fontdict={'weight': 'bold', 'size': 9})\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "方法二"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6    148.0           72.0             35        0  33.6   \n",
       "1            1     85.0           66.0             29        0  26.6   \n",
       "2            8    183.0           64.0              0        0  23.3   \n",
       "3            1     89.0           66.0             23       94  28.1   \n",
       "4            0    137.0           40.0             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "\n",
    "df= pd.read_csv('E:/study/python data analysis/数据挖掘实验二/diabetes2.csv')\n",
    "df=df.loc[:, ~df.columns.str.contains('^Unnamed')]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Pregnancies',axis=1) \n",
    "y = df['Pregnancies'] \n",
    "y = y.map({'p': 'Posionous', 'e': 'Edible'}) \n",
    " \n",
    "cat_cols= X.select_dtypes(include='object').columns.tolist() \n",
    "for col in cat_cols:\n",
    "    print (f\"ol name:{col},N Unique:{X[col].nunique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0    148.0           72.0             35        0  33.6   \n",
       "1     85.0           66.0             29        0  26.6   \n",
       "2    183.0           64.0              0        0  23.3   \n",
       "3     89.0           66.0             23       94  28.1   \n",
       "4    137.0           40.0             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in cat_cols:\n",
    "    X[col]=X[col].astype(\"category\") \n",
    "    X[col]=X[col].cat.codes \n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1st_Component</th>\n",
       "      <th>2n_Component</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.546257</td>\n",
       "      <td>-0.892031</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.603119</td>\n",
       "      <td>0.502532</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.28173</td>\n",
       "      <td>-1.301375</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.612968</td>\n",
       "      <td>0.986998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.728232</td>\n",
       "      <td>2.746665</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  1st_Component 2n_Component class\n",
       "0      1.546257    -0.892031   NaN\n",
       "1     -1.603119     0.502532   NaN\n",
       "2       0.28173    -1.301375   NaN\n",
       "3     -1.612968     0.986998   NaN\n",
       "4      2.728232     2.746665   NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用PCA的降维可视化\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.decomposition import PCA \n",
    " \n",
    "X_std = StandardScaler().fit_transform(X) \n",
    "X_pca = PCA(n_components=2).fit_transform(X_std) \n",
    "X_pca = np.vstack((X_pca.T, y)).T \n",
    " \n",
    "df_pca = pd.DataFrame(X_pca, columns=['1st_Component','2n_Component', 'class']) \n",
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8)) \n",
    "sns.scatterplot(data=df_pca, hue='class', x='1st_Component', y='2n_Component') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dim1</th>\n",
       "      <th>Dim2</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.966825</td>\n",
       "      <td>-17.831484</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-20.358877</td>\n",
       "      <td>11.44589</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.224648</td>\n",
       "      <td>-14.351165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-16.977068</td>\n",
       "      <td>10.163094</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.819184</td>\n",
       "      <td>-16.60968</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Dim1       Dim2 class\n",
       "0  16.966825 -17.831484   NaN\n",
       "1 -20.358877   11.44589   NaN\n",
       "2  35.224648 -14.351165   NaN\n",
       "3 -16.977068  10.163094   NaN\n",
       "4   2.819184  -16.60968   NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#t-SNE降维可视化\n",
    "from sklearn.manifold import TSNE \n",
    " \n",
    "tsne = TSNE(n_components=2) \n",
    "X_tsne = tsne.fit_transform(X_std) \n",
    "X_tsne_data = np.vstack((X_tsne.T, y)).T \n",
    "df_tsne = pd.DataFrame(X_tsne_data, columns=['Dim1', 'Dim2', 'class']) \n",
    "df_tsne.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8)) \n",
    "sns.scatterplot(data=df_tsne, hue='class', x='Dim1', y='Dim2') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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